
# Importing other important libraries
import warnings 
warnings.filterwarnings("ignore")

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder,MinMaxScaler
from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, roc_auc_score, roc_curve, f1_score
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split,cross_val_score,KFold,RandomizedSearchCV
from sklearn.model_selection import cross_val_predict, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler
from imblearn.under_sampling import RandomUnderSampler
from sklearn.utils import resample
from SALib.analyze import sobol
from SALib.sample import saltelli
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

cd

C:\Users\kiran


df = pd.read_csv(r'C:\Users\kiran\Downloads\churn_data_bank\bank_data_train.csv')

df


df.head()


df.shape

(355190, 116)


for column in df.columns:
    print(f"{column}: {df[column].isnull().sum()}")

ID: 0
CR_PROD_CNT_IL: 0
AMOUNT_RUB_CLO_PRC: 38323
PRC_ACCEPTS_A_EMAIL_LINK: 200027
APP_REGISTR_RGN_CODE: 294640
PRC_ACCEPTS_A_POS: 200027
PRC_ACCEPTS_A_TK: 200027
TURNOVER_DYNAMIC_IL_1M: 0
CNT_TRAN_AUT_TENDENCY1M: 278078
SUM_TRAN_AUT_TENDENCY1M: 278078
AMOUNT_RUB_SUP_PRC: 38323
PRC_ACCEPTS_A_AMOBILE: 200027
SUM_TRAN_AUT_TENDENCY3M: 244138
CLNT_TRUST_RELATION: 285769
PRC_ACCEPTS_TK: 200027
PRC_ACCEPTS_A_MTP: 200027
REST_DYNAMIC_FDEP_1M: 0
CNT_TRAN_AUT_TENDENCY3M: 244138
CNT_ACCEPTS_TK: 200027
APP_MARITAL_STATUS: 286956
REST_DYNAMIC_SAVE_3M: 0
CR_PROD_CNT_VCU: 0
REST_AVG_CUR: 0
CNT_TRAN_MED_TENDENCY1M: 286223
APP_KIND_OF_PROP_HABITATION: 295829
CLNT_JOB_POSITION_TYPE: 310409
AMOUNT_RUB_NAS_PRC: 38323
CLNT_JOB_POSITION: 144379
APP_DRIVING_LICENSE: 297933
TRANS_COUNT_SUP_PRC: 38323
APP_EDUCATION: 287086
CNT_TRAN_CLO_TENDENCY1M: 288894
SUM_TRAN_MED_TENDENCY1M: 286223
PRC_ACCEPTS_A_ATM: 200027
PRC_ACCEPTS_MTP: 200027
TRANS_COUNT_NAS_PRC: 38323
APP_TRAVEL_PASS: 297933
CNT_ACCEPTS_MTP: 200027
CR_PROD_CNT_TOVR: 0
APP_CAR: 297934
CR_PROD_CNT_PIL: 0
SUM_TRAN_CLO_TENDENCY1M: 288894
APP_POSITION_TYPE: 294645
TURNOVER_CC: 0
TRANS_COUNT_ATM_PRC: 38323
AMOUNT_RUB_ATM_PRC: 38323
TURNOVER_PAYM: 0
AGE: 0
CNT_TRAN_MED_TENDENCY3M: 239313
CR_PROD_CNT_CC: 0
SUM_TRAN_MED_TENDENCY3M: 239313
REST_DYNAMIC_FDEP_3M: 0
REST_DYNAMIC_IL_1M: 0
APP_EMP_TYPE: 287828
SUM_TRAN_CLO_TENDENCY3M: 240292
LDEAL_TENOR_MAX: 347189
LDEAL_YQZ_CHRG: 353949
CR_PROD_CNT_CCFP: 0
DEAL_YQZ_IR_MAX: 347189
LDEAL_YQZ_COM: 353950
DEAL_YQZ_IR_MIN: 347189
CNT_TRAN_CLO_TENDENCY3M: 240292
REST_DYNAMIC_CUR_1M: 0
REST_AVG_PAYM: 0
LDEAL_TENOR_MIN: 347189
LDEAL_AMT_MONTH: 353302
APP_COMP_TYPE: 287828
LDEAL_GRACE_DAYS_PCT_MED: 0
REST_DYNAMIC_CUR_3M: 0
CNT_TRAN_SUP_TENDENCY3M: 156472
TURNOVER_DYNAMIC_CUR_1M: 0
REST_DYNAMIC_PAYM_3M: 0
SUM_TRAN_SUP_TENDENCY3M: 156472
REST_DYNAMIC_IL_3M: 0
CNT_TRAN_ATM_TENDENCY3M: 99595
CNT_TRAN_ATM_TENDENCY1M: 149316
TURNOVER_DYNAMIC_IL_3M: 0
SUM_TRAN_ATM_TENDENCY3M: 99595
DEAL_GRACE_DAYS_ACC_S1X1: 284741
AVG_PCT_MONTH_TO_PCLOSE: 353562
DEAL_YWZ_IR_MIN: 259477
SUM_TRAN_SUP_TENDENCY1M: 199195
DEAL_YWZ_IR_MAX: 259477
SUM_TRAN_ATM_TENDENCY1M: 149316
REST_DYNAMIC_PAYM_1M: 0
CNT_TRAN_SUP_TENDENCY1M: 199195
DEAL_GRACE_DAYS_ACC_AVG: 285757
TURNOVER_DYNAMIC_CUR_3M: 0
PACK: 0
MAX_PCLOSE_DATE: 353309
LDEAL_YQZ_PC: 352382
CLNT_SETUP_TENOR: 0
DEAL_GRACE_DAYS_ACC_MAX: 285757
TURNOVER_DYNAMIC_PAYM_3M: 0
LDEAL_DELINQ_PER_MAXYQZ: 347189
TURNOVER_DYNAMIC_PAYM_1M: 0
CLNT_SALARY_VALUE: 354478
TRANS_AMOUNT_TENDENCY3M: 51996
MED_DEBT_PRC_YQZ: 347189
TRANS_CNT_TENDENCY3M: 51996
LDEAL_USED_AMT_AVG_YQZ: 347189
REST_DYNAMIC_CC_1M: 0
LDEAL_USED_AMT_AVG_YWZ: 259477
TURNOVER_DYNAMIC_CC_1M: 0
AVG_PCT_DEBT_TO_DEAL_AMT: 353302
LDEAL_ACT_DAYS_ACC_PCT_AVG: 261742
REST_DYNAMIC_CC_3M: 0
MED_DEBT_PRC_YWZ: 259477
LDEAL_ACT_DAYS_PCT_TR3: 261742
LDEAL_ACT_DAYS_PCT_AAVG: 257015
LDEAL_DELINQ_PER_MAXYWZ: 259477
TURNOVER_DYNAMIC_CC_3M: 0
LDEAL_ACT_DAYS_PCT_TR: 261742
LDEAL_ACT_DAYS_PCT_TR4: 261742
LDEAL_ACT_DAYS_PCT_CURR: 261742
TARGET: 0


# Let us drop the data with more than 60 % of the null values to make our dataset more compatible the model.
threshold = len(df) * 0.6
df = df.dropna(thresh=threshold, axis=1)
print(df)

            ID  CR_PROD_CNT_IL  AMOUNT_RUB_CLO_PRC  TURNOVER_DYNAMIC_IL_1M  \
0       146841               0            0.000000                     0.0   
1       146842               0            0.041033                     0.0   
2       146843               0            0.006915                     0.0   
3       146844               0            0.000000                     0.0   
4       146845               0            0.000000                     0.0   
...        ...             ...                 ...                     ...   
355185  590822               0            0.000000                     0.0   
355186  590823               0            0.000000                     0.0   
355187  590825               0            0.041298                     0.0   
355188  590826               0            0.000000                     0.0   
355189  590828               0            0.000000                     0.0   

        AMOUNT_RUB_SUP_PRC  REST_DYNAMIC_FDEP_1M  REST_DYNAMIC_SAVE_3M  \
0                 0.000000                   0.0              0.541683   
1                 0.244678                   0.0              0.000000   
2                 0.000000                   0.0              0.000000   
3                 0.000000                   0.0              0.005874   
4                 0.000000                   0.0              0.000000   
...                    ...                   ...                   ...   
355185            0.000000                   0.0              0.000000   
355186            0.000000                   0.0              0.000000   
355187            0.095187                   0.0              0.000000   
355188            0.000000                   0.0              0.000000   
355189            0.000000                   0.0              0.003551   

        CR_PROD_CNT_VCU   REST_AVG_CUR  AMOUNT_RUB_NAS_PRC  ...  \
0                     0  156067.339767            0.000000  ...   
1                     0    4278.845817            0.000000  ...   
2                     0  112837.062817            0.000000  ...   
3                     0   42902.902883            0.000000  ...   
4                     0   71906.476533            0.000000  ...   
...                 ...            ...                 ...  ...   
355185                0    9697.620867            0.000000  ...   
355186                0  428380.024733            0.262714  ...   
355187                0  224884.436700            0.031179  ...   
355188                0   12080.001833            0.282573  ...   
355189                0   11323.728250            0.000000  ...   

        CLNT_SETUP_TENOR  TURNOVER_DYNAMIC_PAYM_3M  TURNOVER_DYNAMIC_PAYM_1M  \
0               1.593023                       0.0                       0.0   
1               1.587647                       0.0                       0.0   
2               1.587647                       0.0                       0.0   
3               1.583333                       0.0                       0.0   
4               1.583333                       0.0                       0.0   
...                  ...                       ...                       ...   
355185          8.963872                       0.0                       0.0   
355186          8.963872                       0.0                       0.0   
355187          8.966560                       0.0                       0.0   
355188          8.966560                       0.0                       0.0   
355189          8.961184                       0.0                       0.0   

        TRANS_AMOUNT_TENDENCY3M  TRANS_CNT_TENDENCY3M  REST_DYNAMIC_CC_1M  \
0                      0.483032              0.406780                 0.0   
1                      0.394340              0.545455                 0.0   
2                      0.399342              0.297297                 0.0   
3                           NaN                   NaN                 0.0   
4                      0.611610              0.620690                 0.0   
...                         ...                   ...                 ...   
355185                 0.659039              0.785714                 0.0   
355186                 0.652612              0.500000                 0.0   
355187                 0.448386              0.459530                 0.0   
355188                 1.000000              1.000000                 0.0   
355189                      NaN                   NaN                 0.0   

        TURNOVER_DYNAMIC_CC_1M  REST_DYNAMIC_CC_3M  TURNOVER_DYNAMIC_CC_3M  \
0                          0.0                 0.0                     0.0   
1                          0.0                 0.0                     0.0   
2                          0.0                 0.0                     0.0   
3                          0.0                 0.0                     0.0   
4                          0.0                 0.0                     0.0   
...                        ...                 ...                     ...   
355185                     0.0                 0.0                     0.0   
355186                     0.0                 0.0                     0.0   
355187                     0.0                 0.0                     0.0   
355188                     0.0                 0.0                     0.0   
355189                     0.0                 0.0                     0.0   

        TARGET  
0            0  
1            0  
2            0  
3            0  
4            0  
...        ...  
355185       0  
355186       0  
355187       0  
355188       0  
355189       1  

[355190 rows x 46 columns]


df.isnull().sum(axis=0)

ID                              0
CR_PROD_CNT_IL                  0
AMOUNT_RUB_CLO_PRC          38323
TURNOVER_DYNAMIC_IL_1M          0
AMOUNT_RUB_SUP_PRC          38323
REST_DYNAMIC_FDEP_1M            0
REST_DYNAMIC_SAVE_3M            0
CR_PROD_CNT_VCU                 0
REST_AVG_CUR                    0
AMOUNT_RUB_NAS_PRC          38323
TRANS_COUNT_SUP_PRC         38323
TRANS_COUNT_NAS_PRC         38323
CR_PROD_CNT_TOVR                0
CR_PROD_CNT_PIL                 0
TURNOVER_CC                     0
TRANS_COUNT_ATM_PRC         38323
AMOUNT_RUB_ATM_PRC          38323
TURNOVER_PAYM                   0
AGE                             0
CR_PROD_CNT_CC                  0
REST_DYNAMIC_FDEP_3M            0
REST_DYNAMIC_IL_1M              0
CR_PROD_CNT_CCFP                0
REST_DYNAMIC_CUR_1M             0
REST_AVG_PAYM                   0
LDEAL_GRACE_DAYS_PCT_MED        0
REST_DYNAMIC_CUR_3M             0
TURNOVER_DYNAMIC_CUR_1M         0
REST_DYNAMIC_PAYM_3M            0
REST_DYNAMIC_IL_3M              0
CNT_TRAN_ATM_TENDENCY3M     99595
TURNOVER_DYNAMIC_IL_3M          0
SUM_TRAN_ATM_TENDENCY3M     99595
REST_DYNAMIC_PAYM_1M            0
TURNOVER_DYNAMIC_CUR_3M         0
PACK                            0
CLNT_SETUP_TENOR                0
TURNOVER_DYNAMIC_PAYM_3M        0
TURNOVER_DYNAMIC_PAYM_1M        0
TRANS_AMOUNT_TENDENCY3M     51996
TRANS_CNT_TENDENCY3M        51996
REST_DYNAMIC_CC_1M              0
TURNOVER_DYNAMIC_CC_1M          0
REST_DYNAMIC_CC_3M              0
TURNOVER_DYNAMIC_CC_3M          0
TARGET                          0
dtype: int64


df.shape

(355190, 46)


# Dropping Id Column because it is of no use
df.drop('ID', axis=1, inplace=True)


# Filter columns with null values
columns_with_nulls = df.columns[df.isnull().any()]

# Set up subplots for box plots
num_rows = (len(columns_with_nulls) + 2) // 3
fig, axes = plt.subplots(num_rows, 3, figsize=(15, num_rows*5))

# Plot box plots for columns with null values
for i, column in enumerate(columns_with_nulls):
    sns.boxplot(x=df[column], color='red', ax=axes[i // 3, i % 3])
    axes[i // 3, i % 3].set_title(f'Box Plot of {column}')
    axes[i // 3, i % 3].set_xlabel(column)

# Hide empty subplots
if len(columns_with_nulls) < num_rows * 3:
    for j in range(len(columns_with_nulls), num_rows * 3):
        axes[j // 3, j % 3].axis('off')

plt.tight_layout()
plt.show()


# Filter columns with null values
columns_with_nulls = df.columns[df.isnull().any()]

# Set up subplots for histograms
num_rows = (len(columns_with_nulls) + 2) // 3
fig, axes = plt.subplots(num_rows, 3, figsize=(15, num_rows*5))

# Plot histograms for columns with null values
for i, column in enumerate(columns_with_nulls):
    df[column].hist(ax=axes[i // 3, i % 3], color='blue')
    axes[i // 3, i % 3].set_title(f'Histogram of {column}')
    axes[i // 3, i % 3].set_xlabel(column)
    axes[i // 3, i % 3].set_ylabel('Frequency')

# Hide empty subplots
if len(columns_with_nulls) < num_rows * 3:
    for j in range(len(columns_with_nulls), num_rows * 3):
        axes[j // 3, j % 3].axis('off')

plt.tight_layout()
plt.show()


median_clo_prc = df['AMOUNT_RUB_CLO_PRC'].median()
median_sup_prc = df['AMOUNT_RUB_SUP_PRC'].median()
median_nas_prc = df['AMOUNT_RUB_NAS_PRC'].median()
median_atm_prc = df['AMOUNT_RUB_ATM_PRC'].median()

# Fill null values with the respective median
df['AMOUNT_RUB_CLO_PRC'].fillna(median_clo_prc, inplace=True)
df['AMOUNT_RUB_SUP_PRC'].fillna(median_sup_prc, inplace=True)
df['AMOUNT_RUB_NAS_PRC'].fillna(median_nas_prc, inplace=True)
df['AMOUNT_RUB_ATM_PRC'].fillna(median_atm_prc, inplace=True)


mean_sup_prc = df['TRANS_COUNT_SUP_PRC'].mean()
mean_nas_prc = df['TRANS_COUNT_NAS_PRC'].mean()
mean_atm_prc = df['TRANS_COUNT_ATM_PRC'].mean()

# Fill null values with the respective mean
df['TRANS_COUNT_SUP_PRC'].fillna(mean_sup_prc, inplace=True)
df['TRANS_COUNT_NAS_PRC'].fillna(mean_nas_prc, inplace=True)
df['TRANS_COUNT_ATM_PRC'].fillna(mean_atm_prc, inplace=True)


mean_cnt_tran_atm_tendency3m = df['CNT_TRAN_ATM_TENDENCY3M'].mean()
mean_sum_tran_atm_tendency3m = df['SUM_TRAN_ATM_TENDENCY3M'].mean()

# Fill null values with the respective mean
df['CNT_TRAN_ATM_TENDENCY3M'].fillna(mean_cnt_tran_atm_tendency3m, inplace=True)
df['SUM_TRAN_ATM_TENDENCY3M'].fillna(mean_sum_tran_atm_tendency3m, inplace=True)


mean_trans_amount_tendency3m = df['TRANS_AMOUNT_TENDENCY3M'].mean()
mean_trans_cnt_tendency3m = df['TRANS_CNT_TENDENCY3M'].mean()

# Fill null values with the respective mean
df['TRANS_AMOUNT_TENDENCY3M'].fillna(mean_trans_amount_tendency3m, inplace=True)
df['TRANS_CNT_TENDENCY3M'].fillna(mean_trans_cnt_tendency3m, inplace=True)


df.isnull().sum()

CR_PROD_CNT_IL              0
AMOUNT_RUB_CLO_PRC          0
TURNOVER_DYNAMIC_IL_1M      0
AMOUNT_RUB_SUP_PRC          0
REST_DYNAMIC_FDEP_1M        0
REST_DYNAMIC_SAVE_3M        0
CR_PROD_CNT_VCU             0
REST_AVG_CUR                0
AMOUNT_RUB_NAS_PRC          0
TRANS_COUNT_SUP_PRC         0
TRANS_COUNT_NAS_PRC         0
CR_PROD_CNT_TOVR            0
CR_PROD_CNT_PIL             0
TURNOVER_CC                 0
TRANS_COUNT_ATM_PRC         0
AMOUNT_RUB_ATM_PRC          0
TURNOVER_PAYM               0
AGE                         0
CR_PROD_CNT_CC              0
REST_DYNAMIC_FDEP_3M        0
REST_DYNAMIC_IL_1M          0
CR_PROD_CNT_CCFP            0
REST_DYNAMIC_CUR_1M         0
REST_AVG_PAYM               0
LDEAL_GRACE_DAYS_PCT_MED    0
REST_DYNAMIC_CUR_3M         0
TURNOVER_DYNAMIC_CUR_1M     0
REST_DYNAMIC_PAYM_3M        0
REST_DYNAMIC_IL_3M          0
CNT_TRAN_ATM_TENDENCY3M     0
TURNOVER_DYNAMIC_IL_3M      0
SUM_TRAN_ATM_TENDENCY3M     0
REST_DYNAMIC_PAYM_1M        0
TURNOVER_DYNAMIC_CUR_3M     0
PACK                        0
CLNT_SETUP_TENOR            0
TURNOVER_DYNAMIC_PAYM_3M    0
TURNOVER_DYNAMIC_PAYM_1M    0
TRANS_AMOUNT_TENDENCY3M     0
TRANS_CNT_TENDENCY3M        0
REST_DYNAMIC_CC_1M          0
TURNOVER_DYNAMIC_CC_1M      0
REST_DYNAMIC_CC_3M          0
TURNOVER_DYNAMIC_CC_3M      0
TARGET                      0
dtype: int64


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 355190 entries, 0 to 355189
Data columns (total 45 columns):
 #   Column                    Non-Null Count   Dtype  
---  ------                    --------------   -----  
 0   CR_PROD_CNT_IL            355190 non-null  int64  
 1   AMOUNT_RUB_CLO_PRC        355190 non-null  float64
 2   TURNOVER_DYNAMIC_IL_1M    355190 non-null  float64
 3   AMOUNT_RUB_SUP_PRC        355190 non-null  float64
 4   REST_DYNAMIC_FDEP_1M      355190 non-null  float64
 5   REST_DYNAMIC_SAVE_3M      355190 non-null  float64
 6   CR_PROD_CNT_VCU           355190 non-null  int64  
 7   REST_AVG_CUR              355190 non-null  float64
 8   AMOUNT_RUB_NAS_PRC        355190 non-null  float64
 9   TRANS_COUNT_SUP_PRC       355190 non-null  float64
 10  TRANS_COUNT_NAS_PRC       355190 non-null  float64
 11  CR_PROD_CNT_TOVR          355190 non-null  int64  
 12  CR_PROD_CNT_PIL           355190 non-null  int64  
 13  TURNOVER_CC               355190 non-null  float64
 14  TRANS_COUNT_ATM_PRC       355190 non-null  float64
 15  AMOUNT_RUB_ATM_PRC        355190 non-null  float64
 16  TURNOVER_PAYM             355190 non-null  float64
 17  AGE                       355190 non-null  int64  
 18  CR_PROD_CNT_CC            355190 non-null  int64  
 19  REST_DYNAMIC_FDEP_3M      355190 non-null  float64
 20  REST_DYNAMIC_IL_1M        355190 non-null  float64
 21  CR_PROD_CNT_CCFP          355190 non-null  int64  
 22  REST_DYNAMIC_CUR_1M       355190 non-null  float64
 23  REST_AVG_PAYM             355190 non-null  float64
 24  LDEAL_GRACE_DAYS_PCT_MED  355190 non-null  float64
 25  REST_DYNAMIC_CUR_3M       355190 non-null  float64
 26  TURNOVER_DYNAMIC_CUR_1M   355190 non-null  float64
 27  REST_DYNAMIC_PAYM_3M      355190 non-null  float64
 28  REST_DYNAMIC_IL_3M        355190 non-null  float64
 29  CNT_TRAN_ATM_TENDENCY3M   355190 non-null  float64
 30  TURNOVER_DYNAMIC_IL_3M    355190 non-null  float64
 31  SUM_TRAN_ATM_TENDENCY3M   355190 non-null  float64
 32  REST_DYNAMIC_PAYM_1M      355190 non-null  float64
 33  TURNOVER_DYNAMIC_CUR_3M   355190 non-null  float64
 34  PACK                      355190 non-null  object 
 35  CLNT_SETUP_TENOR          355190 non-null  float64
 36  TURNOVER_DYNAMIC_PAYM_3M  355190 non-null  float64
 37  TURNOVER_DYNAMIC_PAYM_1M  355190 non-null  float64
 38  TRANS_AMOUNT_TENDENCY3M   355190 non-null  float64
 39  TRANS_CNT_TENDENCY3M      355190 non-null  float64
 40  REST_DYNAMIC_CC_1M        355190 non-null  float64
 41  TURNOVER_DYNAMIC_CC_1M    355190 non-null  float64
 42  REST_DYNAMIC_CC_3M        355190 non-null  float64
 43  TURNOVER_DYNAMIC_CC_3M    355190 non-null  float64
 44  TARGET                    355190 non-null  int64  
dtypes: float64(36), int64(8), object(1)
memory usage: 121.9+ MB


# Count plot of 'PACK' column
sns.countplot(data=df, x='PACK')
plt.title('Count Plot of PACK')
plt.xlabel('PACK')
plt.ylabel('Count')
plt.show()


# Histogram
sns.histplot(df['AGE'], kde=True, color='purple', bins=20)
plt.title('Histogram of AGE')
plt.xlabel('AGE')
plt.ylabel('Frequency')
plt.grid(True)

plt.show()


# Scatter plot
sns.scatterplot(x=df.index, y='TURNOVER_CC', data=df, color='green')
plt.title('Scatter Plot of TURNOVER_CC')
plt.xlabel('X Axis')
plt.ylabel('TURNOVER_CC')
plt.grid(True)

plt.tight_layout()
plt.show()


# Plot histogram of 'CLNT_SETUP_TENOR'
plt.hist(df['CLNT_SETUP_TENOR'], bins=20, color='skyblue', edgecolor='black')
plt.title('Histogram of CLNT_SETUP_TENOR')
plt.xlabel('CLNT_SETUP_TENOR')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()


plt.figure(figsize=(8, 4))
# Scatter plot
sns.scatterplot(x='TURNOVER_CC', y='TURNOVER_PAYM', data=df, color='blue')
plt.title('Scatter Plot between TURNOVER_CC and TURNOVER_PAYM')
plt.xlabel('TURNOVERCC')
plt.ylabel('TURNOVER_PAYM')
plt.grid(True)

plt.show()


plt.figure(figsize=(8, 6))
# Scatter plot
sns.scatterplot(x='TRANS_AMOUNT_TENDENCY3M', y='TARGET', data=df, color='blue')
plt.title('Scatter Plot between TRANS_AMOUNT_TENDENCY3M and TARGET')
plt.xlabel('TRANS_AMOUNT_TENDENCY3M')
plt.ylabel('TARGET')
plt.grid(True)
plt.show()


plt.figure(figsize=(8, 6))

# Scatter plot
sns.scatterplot(x='TARGET', y='REST_AVG_PAYM', data=df, color='orange')
plt.title('Scatter Plot between TARGET and REST_AVG_PAYM')
plt.xlabel('TARGET')
plt.ylabel('REST_AVG_PAYM')
plt.grid(True)

plt.show()


df.shape

(355190, 45)


# Select only numeric columns
numeric_df = df.select_dtypes(include='number')

# Calculating correlation for numerical features
correlation = numeric_df.corr()

# Get the number of features
num_features = len(correlation)

# Split the features into two equal parts
half_features = num_features // 2

# Split the correlation matrix into two parts
correlation_1 = correlation.iloc[:half_features, :half_features]
correlation_2 = correlation.iloc[half_features:, half_features:]


# Create masks to hide the upper triangle
mask_1 = np.triu(np.ones_like(correlation_1, dtype=bool))
mask_2 = np.triu(np.ones_like(correlation_2, dtype=bool))

# Plotting heatmap for the first part of the correlation matrix
plt.figure(figsize=(20, 20))
sns.heatmap(correlation_1, mask=mask_1, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Chart (First Half of Features)')
plt.show()


# Plotting heatmap for the second part of the correlation matrix
plt.figure(figsize=(20, 20))
sns.heatmap(correlation_2, mask=mask_2, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Chart (Second Half of Features)')
plt.show()


# One hot encoding of "PACK"
encoder = OneHotEncoder()
ohreq = df[['PACK']]
oh_encoded = encoder.fit_transform(ohreq)
oh_df = pd.DataFrame(oh_encoded.toarray(), columns=encoder.get_feature_names_out(['PACK']))
oh_df.index = df.index
df = pd.concat([df, oh_df], axis=1)

# Dropping "PACK" feature afer doing its one hot encoding
df.drop('PACK', axis=1, inplace=True)


df['total_turnover'] = df['TURNOVER_DYNAMIC_IL_1M']  + df['TURNOVER_CC'] + df['TURNOVER_PAYM'] + df['TRANS_AMOUNT_TENDENCY3M']


df['TOTAL_PROD_CNT'] = df['CR_PROD_CNT_IL'] + df['CR_PROD_CNT_VCU'] + df['CR_PROD_CNT_TOVR'] + df['CR_PROD_CNT_PIL'] + df['CR_PROD_CNT_CC'] + df['CR_PROD_CNT_CCFP']


# Define bin edges and label
bin_edges = [168, 300, 500, 700, 900, 1128]
bin_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4', 'Group 5']

# Bin the 'AGE' feature
df['AGE_GROUP'] = pd.cut(df['AGE'], bins=bin_edges, labels=bin_labels)


df.drop(columns=['AGE'], inplace=True)


df['TARGET'].value_counts()

TARGET
0    326265
1     28925
Name: count, dtype: int64


target_counts = df['TARGET'].value_counts()

# Plot the bar graph
plt.figure(figsize=(8, 6))
target_counts.plot(kind='bar', color=['blue', 'red'])
plt.title('Distribution of TARGET Variable')
plt.xlabel('TARGET')
plt.ylabel('Count')
plt.xticks(rotation=0)
plt.show()


df.isnull().sum()

CR_PROD_CNT_IL               0
AMOUNT_RUB_CLO_PRC           0
TURNOVER_DYNAMIC_IL_1M       0
AMOUNT_RUB_SUP_PRC           0
REST_DYNAMIC_FDEP_1M         0
REST_DYNAMIC_SAVE_3M         0
CR_PROD_CNT_VCU              0
REST_AVG_CUR                 0
AMOUNT_RUB_NAS_PRC           0
TRANS_COUNT_SUP_PRC          0
TRANS_COUNT_NAS_PRC          0
CR_PROD_CNT_TOVR             0
CR_PROD_CNT_PIL              0
TURNOVER_CC                  0
TRANS_COUNT_ATM_PRC          0
AMOUNT_RUB_ATM_PRC           0
TURNOVER_PAYM                0
CR_PROD_CNT_CC               0
REST_DYNAMIC_FDEP_3M         0
REST_DYNAMIC_IL_1M           0
CR_PROD_CNT_CCFP             0
REST_DYNAMIC_CUR_1M          0
REST_AVG_PAYM                0
LDEAL_GRACE_DAYS_PCT_MED     0
REST_DYNAMIC_CUR_3M          0
TURNOVER_DYNAMIC_CUR_1M      0
REST_DYNAMIC_PAYM_3M         0
REST_DYNAMIC_IL_3M           0
CNT_TRAN_ATM_TENDENCY3M      0
TURNOVER_DYNAMIC_IL_3M       0
SUM_TRAN_ATM_TENDENCY3M      0
REST_DYNAMIC_PAYM_1M         0
TURNOVER_DYNAMIC_CUR_3M      0
CLNT_SETUP_TENOR             0
TURNOVER_DYNAMIC_PAYM_3M     0
TURNOVER_DYNAMIC_PAYM_1M     0
TRANS_AMOUNT_TENDENCY3M      0
TRANS_CNT_TENDENCY3M         0
REST_DYNAMIC_CC_1M           0
TURNOVER_DYNAMIC_CC_1M       0
REST_DYNAMIC_CC_3M           0
TURNOVER_DYNAMIC_CC_3M       0
TARGET                       0
PACK_101                     0
PACK_102                     0
PACK_103                     0
PACK_104                     0
PACK_105                     0
PACK_107                     0
PACK_108                     0
PACK_109                     0
PACK_301                     0
PACK_K01                     0
PACK_M01                     0
PACK_O01                     0
total_turnover               0
TOTAL_PROD_CNT               0
AGE_GROUP                   20
dtype: int64


plt.figure(figsize=(8, 6))

# Countplot
sns.countplot(x='AGE_GROUP', data=df, palette='Set2')
plt.title('Distribution of AGE_GROUP')
plt.xlabel('AGE_GROUP')
plt.ylabel('Count')
plt.grid(True)

plt.tight_layout()
plt.show()


mode_age = df['AGE_GROUP'].mode()

# Fill null values with the respective mean
df['AGE_GROUP'].fillna(mode_age, inplace=True)


# Using one hot encoding to create new features and coverting it into numerical column
encoder = OneHotEncoder()

ohreq = df[['AGE_GROUP']]
oh_encoded = encoder.fit_transform(ohreq)
oh_df = pd.DataFrame(oh_encoded.toarray(), columns=encoder.get_feature_names_out(['AGE_GROUP']))
oh_df.index = df.index
df = pd.concat([df, oh_df], axis=1)


for column in df.columns:
    print(f"{column}: {df[column].isnull().sum()}")

CR_PROD_CNT_IL: 0
AMOUNT_RUB_CLO_PRC: 0
TURNOVER_DYNAMIC_IL_1M: 0
AMOUNT_RUB_SUP_PRC: 0
REST_DYNAMIC_FDEP_1M: 0
REST_DYNAMIC_SAVE_3M: 0
CR_PROD_CNT_VCU: 0
REST_AVG_CUR: 0
AMOUNT_RUB_NAS_PRC: 0
TRANS_COUNT_SUP_PRC: 0
TRANS_COUNT_NAS_PRC: 0
CR_PROD_CNT_TOVR: 0
CR_PROD_CNT_PIL: 0
TURNOVER_CC: 0
TRANS_COUNT_ATM_PRC: 0
AMOUNT_RUB_ATM_PRC: 0
TURNOVER_PAYM: 0
CR_PROD_CNT_CC: 0
REST_DYNAMIC_FDEP_3M: 0
REST_DYNAMIC_IL_1M: 0
CR_PROD_CNT_CCFP: 0
REST_DYNAMIC_CUR_1M: 0
REST_AVG_PAYM: 0
LDEAL_GRACE_DAYS_PCT_MED: 0
REST_DYNAMIC_CUR_3M: 0
TURNOVER_DYNAMIC_CUR_1M: 0
REST_DYNAMIC_PAYM_3M: 0
REST_DYNAMIC_IL_3M: 0
CNT_TRAN_ATM_TENDENCY3M: 0
TURNOVER_DYNAMIC_IL_3M: 0
SUM_TRAN_ATM_TENDENCY3M: 0
REST_DYNAMIC_PAYM_1M: 0
TURNOVER_DYNAMIC_CUR_3M: 0
CLNT_SETUP_TENOR: 0
TURNOVER_DYNAMIC_PAYM_3M: 0
TURNOVER_DYNAMIC_PAYM_1M: 0
TRANS_AMOUNT_TENDENCY3M: 0
TRANS_CNT_TENDENCY3M: 0
REST_DYNAMIC_CC_1M: 0
TURNOVER_DYNAMIC_CC_1M: 0
REST_DYNAMIC_CC_3M: 0
TURNOVER_DYNAMIC_CC_3M: 0
TARGET: 0
PACK_101: 0
PACK_102: 0
PACK_103: 0
PACK_104: 0
PACK_105: 0
PACK_107: 0
PACK_108: 0
PACK_109: 0
PACK_301: 0
PACK_K01: 0
PACK_M01: 0
PACK_O01: 0
total_turnover: 0
TOTAL_PROD_CNT: 0
AGE_GROUP: 20
AGE_GROUP_Group 1: 0
AGE_GROUP_Group 2: 0
AGE_GROUP_Group 3: 0
AGE_GROUP_Group 4: 0
AGE_GROUP_Group 5: 0
AGE_GROUP_nan: 0


df.drop('AGE_GROUP', axis=1,inplace=True)
df.drop('AGE_GROUP_nan', axis=1,inplace=True)


# Target variable
y = df['TARGET']
  
 # Features
X = df.drop(columns=['TARGET'])


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)


def perform_random_undersampling(X_train, y_train):
    """
    Undersample the majority class using RandomUnderSampler.

    Parameters:
    X_train : DataFrame or array-like
        Features of the training data.
    y_train : Series or array-like
        Target labels of the training data.

    Returns:
    X_train_undersampled : DataFrame or array-like
        Undersampled features of the training data.
    y_train_undersampled : Series or array-like
        Undersampled target labels of the training data.
    """
    # Initialize RandomUnderSampler
    undersampler = RandomUnderSampler(random_state=42)
    
    # Perform undersampling
    X_train_undersampled, y_train_undersampled = undersampler.fit_resample(X_train, y_train)
    
    return X_train_undersampled, y_train_undersampled


X_train_undersampled, y_train_undersampled = perform_random_undersampling(X_train, y_train)


# Before undersampling
print("Before undersampling:")
print(y_train.value_counts())

# After undersampling
print("\nAfter undersampling:")
print(y_train_undersampled.value_counts())

Before undersampling:
TARGET
0    228299
1     20334
Name: count, dtype: int64

After undersampling:
TARGET
0    20334
1    20334
Name: count, dtype: int64


# Initialize the StandardScaler object
scaler = StandardScaler()

# Fit and transform the training features
X_train_scaled = scaler.fit_transform(X_train_undersampled)

# Transform the test features using the parameters learned from the training data
X_test_scaled = scaler.transform(X_test)


X_train_scaled.shape

(40668, 61)


y_train_undersampled.shape

(40668,)


# Set up Logistic Regression model
log_reg = LogisticRegression()


# Set up parameter grid for GridSearchCV
param_grid = {'C': [0.1, 1, 10],'penalty': ['l1', 'l2']}
# Set up GridSearchCV with k-fold cross-validation
grid_search = GridSearchCV(log_reg, param_grid, cv=10, scoring='accuracy', n_jobs=-1)


# Fit GridSearchCV to training data
grid_search.fit(X_train_scaled, y_train_undersampled)

GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,
             param_grid={'C': [0.1, 1, 10], 'penalty': ['l1', 'l2']},
             scoring='accuracy')

GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,
             param_grid={'C': [0.1, 1, 10], 'penalty': ['l1', 'l2']},
             scoring='accuracy')

LogisticRegression()

LogisticRegression()


# Get the best parameters
best_params = grid_search.best_params_
print("Best Parameters:", best_params)

# Use best parameters to build the final model
final_model = LogisticRegression(**best_params)
final_model.fit(X_train_scaled, y_train_undersampled)

Best Parameters: {'C': 0.1, 'penalty': 'l2'}

LogisticRegression(C=0.1)

LogisticRegression(C=0.1)


# Predictions on the training set using the final Logistic Regression model
y_train_pred = final_model.predict(X_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob = final_model.predict_proba(X_train_scaled)[:, 1]


# Compute the confusion matrix on the training set
cm_train = confusion_matrix(y_train_undersampled, y_train_pred)

# Compute accuracy, precision, recall, and F1 score on the training set
accuracy_train = (cm_train[0,0] + cm_train[1,1]) / np.sum(cm_train)
precision_train = precision_score(y_train_undersampled, y_train_pred)
recall_train = recall_score(y_train_undersampled, y_train_pred)
f1_train = f1_score(y_train_undersampled, y_train_pred)

# Compute the ROC AUC score on the training set
roc_auc_train = roc_auc_score(y_train_undersampled, y_train_prob)

# Compute ROC curve for training set
fpr_train, tpr_train, _ = roc_curve(y_train_undersampled, y_train_prob)

print("Confusion Matrix (Training Set):\n", cm_train)
print("Accuracy (Train):", accuracy_train)
print("Precision (Train):", precision_train)
print("Recall (Train):", recall_train)
print("F1 Score (Train):", f1_train)
print("ROC AUC Score (Train):", roc_auc_train)

Confusion Matrix (Training Set):
 [[12672  7662]
 [ 6004 14330]]
Accuracy (Train): 0.6639618373168092
Precision (Train): 0.6516005820298291
Recall (Train): 0.7047309924264779
F1 Score (Train): 0.6771251712895148
ROC AUC Score (Train): 0.7298662909716576


X_test_scaled.shape

(106557, 61)


y_test.shape

(106557,)


# Predictions on the test set using the final Logistic Regression model
y_test_pred = final_model.predict(X_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob = final_model.predict_proba(X_test_scaled)[:, 1]


# Compute the confusion matrix on the test set
cm_test = confusion_matrix(y_test, y_test_pred)

# Compute accuracy, precision, recall, and F1 score on the test set
accuracy_test = (cm_test[0,0] + cm_test[1,1]) / np.sum(cm_test)
precision_test = precision_score(y_test, y_test_pred)
recall_test = recall_score(y_test, y_test_pred)
f1_test = f1_score(y_test, y_test_pred)

# Compute the ROC AUC score on the test set
roc_auc_test = roc_auc_score(y_test, y_test_prob)

print("Confusion Matrix (Test Set):\n", cm_test)
print("Accuracy (Test):", accuracy_test)
print("Precision (Test):", precision_test)
print("Recall (Test):", recall_test)
print("F1 Score (Test):", f1_test)
print("ROC AUC Score (Test):", roc_auc_test)

Confusion Matrix (Test Set):
 [[60654 37312]
 [ 2577  6014]]
Accuracy (Test): 0.6256557523203544
Precision (Test): 0.13880810598716706
Recall (Test): 0.700034920265394
F1 Score (Test): 0.23167748521678833
ROC AUC Score (Test): 0.7230050413871172


# Compute ROC curve for test set
fpr_test, tpr_test, _ = roc_curve(y_test, y_test_prob)

# Plot ROC curve for both training and test sets
plt.figure(figsize=(10, 5))
plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')
plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()


coefficients = final_model.coef_[0]

# Create a DataFrame to store feature names and their coefficients
feature_coefficients = pd.DataFrame({'Feature': X_train.columns, 'Coefficient': coefficients})

# Sort the DataFrame by absolute coefficient values to identify the most important features
feature_coefficients['Absolute Coefficient'] = np.abs(feature_coefficients['Coefficient'])
feature_coefficients = feature_coefficients.sort_values(by='Absolute Coefficient', ascending=False)

# Print the top features
print("Top features:")
print(feature_coefficients.head(10))  # Adjust the number of top features as needed

# Plot feature coefficients
plt.figure(figsize=(20, 16))
plt.barh(feature_coefficients['Feature'], feature_coefficients['Coefficient'])
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Feature Coefficients')
plt.show()

Top features:
                     Feature  Coefficient  Absolute Coefficient
7               REST_AVG_CUR    -1.211265              1.211265
25   TURNOVER_DYNAMIC_CUR_1M     0.408601              0.408601
14       TRANS_COUNT_ATM_PRC     0.380209              0.380209
21       REST_DYNAMIC_CUR_1M    -0.344919              0.344919
22             REST_AVG_PAYM    -0.278476              0.278476
31      REST_DYNAMIC_PAYM_1M    -0.236047              0.236047
35  TURNOVER_DYNAMIC_PAYM_1M     0.226137              0.226137
33          CLNT_SETUP_TENOR    -0.222930              0.222930
24       REST_DYNAMIC_CUR_3M    -0.155333              0.155333
9        TRANS_COUNT_SUP_PRC     0.141405              0.141405


# Define the input parameter space for sensitivity analysis

column_names = X_train.columns.tolist()



problem = {
    'num_vars': len(column_names),
    'names': column_names,
    'bounds': [[-3, 3] for _ in range(len(column_names))]
}

# Sample the input parameter space using Saltelli's method
param_values = saltelli.sample(problem, 1000)

# Perform model runs with each parameter sample
Y = np.empty([param_values.shape[0]])

for i, X in enumerate(param_values):
    # Predict using the model
    Y[i] = final_model.predict(X.reshape(1, -1))

# Perform Sobol sensitivity analysis
sobol_results = sobol.analyze(problem, Y, print_to_console=True)

# You can print the sensitivity indices if needed
print("Sobol First Order Indices:", sobol_results['S1'])
print("Sobol Total Order Indices:", sobol_results['ST'])

                              ST   ST_conf
CR_PROD_CNT_IL          0.028027  0.014524
AMOUNT_RUB_CLO_PRC      0.020019  0.013543
TURNOVER_DYNAMIC_IL_1M  0.030029  0.013766
AMOUNT_RUB_SUP_PRC      0.020019  0.012655
REST_DYNAMIC_FDEP_1M    0.024023  0.013705
...                          ...       ...
AGE_GROUP_Group 1       0.024023  0.011789
AGE_GROUP_Group 2       0.006006  0.007348
AGE_GROUP_Group 3       0.030029  0.014610
AGE_GROUP_Group 4       0.002002  0.003899
AGE_GROUP_Group 5       0.016015  0.008952

[61 rows x 2 columns]
                              S1   S1_conf
CR_PROD_CNT_IL          0.003882  0.013891
AMOUNT_RUB_CLO_PRC     -0.000244  0.011314
TURNOVER_DYNAMIC_IL_1M -0.005823  0.013952
AMOUNT_RUB_SUP_PRC     -0.000244  0.013105
REST_DYNAMIC_FDEP_1M    0.004004  0.012928
...                          ...       ...
AGE_GROUP_Group 1       0.000244  0.012042
AGE_GROUP_Group 2      -0.001941  0.006317
AGE_GROUP_Group 3      -0.006067  0.015855
AGE_GROUP_Group 4       0.002063  0.004018
AGE_GROUP_Group 5      -0.003882  0.010526

[61 rows x 2 columns]
                                                S2   S2_conf
(CR_PROD_CNT_IL, AMOUNT_RUB_CLO_PRC)      0.002185  0.016629
(CR_PROD_CNT_IL, TURNOVER_DYNAMIC_IL_1M)  0.006189  0.014603
(CR_PROD_CNT_IL, AMOUNT_RUB_SUP_PRC)      0.006189  0.014603
(CR_PROD_CNT_IL, REST_DYNAMIC_FDEP_1M)    0.002185  0.016629
(CR_PROD_CNT_IL, REST_DYNAMIC_SAVE_3M)    0.006189  0.014603
...                                            ...       ...
(AGE_GROUP_Group 2, AGE_GROUP_Group 4)    0.001941  0.006317
(AGE_GROUP_Group 2, AGE_GROUP_Group 5)    0.001941  0.006317
(AGE_GROUP_Group 3, AGE_GROUP_Group 4)    0.008008  0.018562
(AGE_GROUP_Group 3, AGE_GROUP_Group 5)    0.012012  0.016941
(AGE_GROUP_Group 4, AGE_GROUP_Group 5)   -0.002063  0.004018

[1830 rows x 2 columns]
Sobol First Order Indices: [ 3.88192408e-03 -2.43847240e-04 -5.82288612e-03 -2.43847240e-04
  4.00384770e-03 -2.18480928e-03 -1.94096204e-03  5.01212504e-01
  1.94096204e-03 -1.00705811e-02 -2.18480928e-03 -4.00384770e-03
  3.88192408e-03 -3.76000046e-03  1.45621232e-02  1.94096204e-03
 -1.94096204e-03 -5.94480974e-03  6.18865698e-03 -1.94096204e-03
 -2.06288566e-03  2.99678959e-02  1.46840469e-02  4.00384770e-03
 -3.88192408e-03  4.30767516e-02  0.00000000e+00  7.15423005e-03
  9.94865743e-03 -4.00384770e-03 -4.00384770e-03  2.62078955e-02
  2.43847240e-04 -1.09240464e-02 -1.61373144e-02 -2.18480928e-03
  1.94096204e-03 -3.88192408e-03 -1.94096204e-03  1.81903842e-03
 -1.81903842e-03 -4.00384770e-03 -6.18865698e-03  4.00384770e-03
 -2.06288566e-03 -4.12577132e-03 -4.24769494e-03 -6.06673336e-03
  0.00000000e+00  0.00000000e+00 -1.94096204e-03 -2.18480928e-03
 -7.88577178e-03 -4.00384770e-03  6.06673336e-03  4.44104190e-19
  2.43847240e-04 -1.94096204e-03 -6.06673336e-03  2.06288566e-03
 -3.88192408e-03]
Sobol Total Order Indices: [0.02802693 0.02001924 0.03002886 0.02001924 0.02402309 0.03403271
 0.01801731 0.82479263 0.00600577 0.06606349 0.03002886 0.01601539
 0.02001924 0.01601539 0.23422509 0.00600577 0.01401347 0.01000962
 0.05805579 0.01000962 0.01000962 0.17016353 0.17016353 0.0080077
 0.08808465 0.23022124 0.02802693 0.06005772 0.03803655 0.03603463
 0.01201154 0.09809427 0.02402309 0.11811351 0.04804617 0.12211735
 0.00200192 0.03203078 0.02602501 0.0420404  0.01801731 0.00400385
 0.02202116 0.01601539 0.03002886 0.02802693 0.04003848 0.03803655
 0.         0.0080077  0.00200192 0.04604425 0.02402309 0.03603463
 0.01000962 0.02802693 0.02402309 0.00600577 0.03002886 0.00200192
 0.01601539]


most_influential_params = np.argsort(sobol_results['ST'])[::-1]


# Visualizing Total-Order Sensitivity Indices using a bar plot
plt.figure(figsize=(15, 8))
plt.bar(problem['names'], sobol_results['ST'])
plt.title('Total-Order Sensitivity Indices')
plt.xlabel('Parameter')
plt.ylabel('Total-Order Sensitivity Index')
plt.xticks(rotation=90)
plt.show()

threshold = 0.1
selected_features = [problem['names'][i] for i in range(len(problem['names'])) if sobol_results['ST'][i] > threshold]


print(selected_features)
X_train_selected = X_train[selected_features]
X_test_selected = X_test[selected_features]

['REST_AVG_CUR', 'TRANS_COUNT_ATM_PRC', 'REST_DYNAMIC_CUR_1M', 'REST_AVG_PAYM', 'TURNOVER_DYNAMIC_CUR_1M', 'CLNT_SETUP_TENOR', 'TURNOVER_DYNAMIC_PAYM_1M']


X_train_usampled, y_train_usampled = perform_random_undersampling(X_train_selected, y_train)


# Before undersampling
print("Before undersampling:")
print(y_train.value_counts())

# After undersampling
print("\nAfter undersampling:")
print(y_train_undersampled.value_counts())

Before undersampling:
TARGET
0    228299
1     20334
Name: count, dtype: int64

After undersampling:
TARGET
0    20334
1    20334
Name: count, dtype: int64


# Initialize the StandardScaler object
scaler2 = StandardScaler()

# Fit and transform the training features
X_train_scaled = scaler2.fit_transform(X_train_usampled)

# Transform the test features using the parameters learned from the training data
X_test_scaled = scaler2.transform(X_test_selected)


# Set up Logistic Regression model
log_reg = LogisticRegression()


# Set up parameter grid for GridSearchCV
param_grid = {'C': [0.1, 1, 10],'penalty': ['l1', 'l2']}
# Set up GridSearchCV with k-fold cross-validation
grid_search = GridSearchCV(log_reg, param_grid, cv=10, scoring='accuracy', n_jobs=-1)


# Fit GridSearchCV to training data
grid_search.fit(X_train_scaled, y_train_usampled)

GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,
             param_grid={'C': [0.1, 1, 10], 'penalty': ['l1', 'l2']},
             scoring='accuracy')

GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,
             param_grid={'C': [0.1, 1, 10], 'penalty': ['l1', 'l2']},
             scoring='accuracy')

LogisticRegression()

LogisticRegression()


X_train_scaled.shape

(40668, 7)


y_train_usampled.shape

(40668,)


# Get the best parameters
best_params = grid_search.best_params_
print("Best Parameters:", best_params)

# Use best parameters to build the final model
final_model = LogisticRegression(**best_params)
final_model.fit(X_train_scaled, y_train_usampled)

Best Parameters: {'C': 10, 'penalty': 'l2'}

LogisticRegression(C=10)

LogisticRegression(C=10)


# Predictions on the training set using the final Logistic Regression model
y_train_pred = final_model.predict(X_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob = final_model.predict_proba(X_train_scaled)[:, 1]


# Compute the confusion matrix on the training set
cm_train = confusion_matrix(y_train_usampled, y_train_pred)

# Compute accuracy, precision, recall, and F1 score on the training set
accuracy_train = (cm_train[0, 0] + cm_train[1, 1]) / np.sum(cm_train)
precision_train = precision_score(y_train_usampled, y_train_pred)
recall_train = recall_score(y_train_usampled, y_train_pred)
f1_train = f1_score(y_train_usampled, y_train_pred)

# Compute the ROC AUC score on the training set
roc_auc_train = roc_auc_score(y_train_usampled, y_train_prob)

# Compute ROC curve for training set
fpr_train, tpr_train, _ = roc_curve(y_train_usampled, y_train_prob)

print("Confusion Matrix (Training Set):\n", cm_train)
print("Accuracy (Train):", accuracy_train)
print("Precision (Train):", precision_train)
print("Recall (Train):", recall_train)
print("F1 Score (Train):", f1_train)
print("ROC AUC Score (Train):", roc_auc_train)

Confusion Matrix (Training Set):
 [[12340  7994]
 [ 5952 14382]]
Accuracy (Train): 0.6570768171535359
Precision (Train): 0.6427422238112264
Recall (Train): 0.7072882856299794
F1 Score (Train): 0.6734722547412784
ROC AUC Score (Train): 0.716468372977995


X_test_scaled.shape

(106557, 7)


y_test.shape

(106557,)


# Predictions on the test set using the final Logistic Regression model
y_test_pred = final_model.predict(X_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob = final_model.predict_proba(X_test_scaled)[:, 1]


# Compute the confusion matrix on the test set
cm_test = confusion_matrix(y_test, y_test_pred)

# Compute accuracy, precision, recall, and F1 score on the test set
accuracy_test = (cm_test[0, 0] + cm_test[1, 1]) / np.sum(cm_test)
precision_test = precision_score(y_test, y_test_pred)
recall_test = recall_score(y_test, y_test_pred)
f1_test = f1_score(y_test, y_test_pred)

# Compute the ROC AUC score on the test set
roc_auc_test = roc_auc_score(y_test, y_test_prob)

print("Confusion Matrix (Test Set):\n", cm_test)
print("Accuracy (Test):", accuracy_test)
print("Precision (Test):", precision_test)
print("Recall (Test):", recall_test)
print("F1 Score (Test):", f1_test)
print("ROC AUC Score (Test):", roc_auc_test)

Confusion Matrix (Test Set):
 [[59006 38960]
 [ 2534  6057]]
Accuracy (Test): 0.6105933913304616
Precision (Test): 0.13454917031343713
Recall (Test): 0.7050401583052032
F1 Score (Test): 0.2259737352633935
ROC AUC Score (Test): 0.7128585583248431


# Compute ROC curve for test set
fpr_test, tpr_test, _ = roc_curve(y_test, y_test_prob)

# Plot ROC curve for both training and test sets
plt.figure(figsize=(10, 5))
plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')
plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()


feature_coefficients = final_model.coef_[0]

# Create a DataFrame to store feature names and coefficients
coefficients_df = pd.DataFrame({'Feature': X_train_selected.columns, 'Coefficient': feature_coefficients})
coefficients_df = coefficients_df.sort_values(by='Coefficient', ascending=False)

# Print the feature coefficients
print("Feature Coefficients:")
print(coefficients_df)

# Plot feature coefficients
plt.figure(figsize=(8,5))
plt.barh(coefficients_df['Feature'], coefficients_df['Coefficient'])
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Feature Coefficients')
plt.show()

Feature Coefficients:
                    Feature  Coefficient
4   TURNOVER_DYNAMIC_CUR_1M     0.456687
1       TRANS_COUNT_ATM_PRC     0.312466
6  TURNOVER_DYNAMIC_PAYM_1M     0.217990
3             REST_AVG_PAYM    -0.189524
5          CLNT_SETUP_TENOR    -0.225847
2       REST_DYNAMIC_CUR_1M    -0.415855
0              REST_AVG_CUR    -1.298116


X_train_undersampled, y_train_undersampled = perform_random_undersampling(X_train, y_train)


# Initialize the StandardScaler object
scaler = StandardScaler()

# Fit and transform the training features
X_train_scaled = scaler.fit_transform(X_train_undersampled)

# Transform the test features using the parameters learned from the training data
X_test_scaled = scaler.transform(X_test)


# Specify the desired sample size
sample_size = 1000  # Adjust this according to your requirements

# Randomly sample the training data
X_train_sampled, y_train_sampled = resample(X_train_scaled, y_train_undersampled, n_samples=sample_size, random_state=42)


# Initialize the Support Vector Classifier
svm = SVC(probability=True)


# Set up parameter grid for GridSearchCV
param_grid = {'C': [0.1, 1, 10], 'gamma': [0.1, 0.01, 0.001], 'kernel': ['linear', 'rbf']}

# Set up GridSearchCV with k-fold cross-validation
grid_search_svm = GridSearchCV(svm, param_grid, cv=10, scoring='accuracy', n_jobs=-1)

# Fit GridSearchCV to sampled training data
grid_search_svm.fit(X_train_sampled, y_train_sampled)

# Get the best parameters
best_params_svm = grid_search_svm.best_params_
print("Best Parameters:", best_params_svm)

# Use best parameters to build the final SVM model
final_svm_model = SVC(**best_params_svm, probability=True)
final_svm_model.fit(X_train_sampled, y_train_sampled)

Best Parameters: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}

SVC(C=10, gamma=0.01, probability=True)

SVC(C=10, gamma=0.01, probability=True)


X_train_scaled.shape

(40668, 61)


y_test.shape

(106557,)


# Predictions on the training set using the final SVM model
y_train_pred_svm = final_svm_model.predict(X_train_sampled)

# Probabilities for the positive class on the training set
y_train_prob_svm = final_svm_model.predict_proba(X_train_sampled)[:, 1]

# Compute the confusion matrix on the training set for SVM
cm_train_svm = confusion_matrix(y_train_sampled, y_train_pred_svm)

# Compute accuracy, precision, recall, and F1 score on the training set for SVM
accuracy_train_svm = (cm_train_svm[0, 0] + cm_train_svm[1, 1]) / np.sum(cm_train_svm)
precision_train_svm = precision_score(y_train_sampled, y_train_pred_svm)
recall_train_svm = recall_score(y_train_sampled, y_train_pred_svm)
f1_train_svm = f1_score(y_train_sampled, y_train_pred_svm)

# Compute the ROC AUC score on the training set for SVM
roc_auc_train_svm = roc_auc_score(y_train_sampled, y_train_prob_svm)

# Compute ROC curve for training set for SVM
fpr_train_svm, tpr_train_svm, _ = roc_curve(y_train_sampled, y_train_prob_svm)

print("Confusion Matrix (Training Set - SVM):\n", cm_train_svm)
print("Accuracy (Train - SVM):", accuracy_train_svm)
print("Precision (Train - SVM):", precision_train_svm)
print("Recall (Train - SVM):", recall_train_svm)
print("F1 Score (Train - SVM):", f1_train_svm)
print("ROC AUC Score (Train - SVM):", roc_auc_train_svm)

Confusion Matrix (Training Set - SVM):
 [[444  72]
 [ 93 391]]
Accuracy (Train - SVM): 0.835
Precision (Train - SVM): 0.8444924406047516
Recall (Train - SVM): 0.8078512396694215
F1 Score (Train - SVM): 0.8257655755015839
ROC AUC Score (Train - SVM): 0.9114032769556025


# Predictions on the test set using the final SVM model
y_test_pred_svm = final_svm_model.predict(X_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob_svm = final_svm_model.predict_proba(X_test_scaled)[:, 1]

# Compute the confusion matrix on the test set for SVM
cm_test_svm = confusion_matrix(y_test, y_test_pred_svm)

# Compute accuracy, precision, recall, and F1 score on the test set for SVM
accuracy_test_svm = (cm_test_svm[0, 0] + cm_test_svm[1, 1]) / np.sum(cm_test_svm)
precision_test_svm = precision_score(y_test, y_test_pred_svm)
recall_test_svm = recall_score(y_test, y_test_pred_svm)
f1_test_svm = f1_score(y_test, y_test_pred_svm)

# Compute the ROC AUC score on the test set for SVM
roc_auc_test_svm = roc_auc_score(y_test, y_test_prob_svm)

print("Confusion Matrix (Test Set - SVM):\n", cm_test_svm)
print("Accuracy (Test - SVM):", accuracy_test_svm)
print("Precision (Test - SVM):", precision_test_svm)
print("Recall (Test - SVM):", recall_test_svm)
print("F1 Score (Test - SVM):", f1_test_svm)
print("ROC AUC Score (Test - SVM):", roc_auc_test_svm)

Confusion Matrix (Test Set - SVM):
 [[63777 34189]
 [ 3423  5168]]
Accuracy (Test - SVM): 0.6470245971639592
Precision (Test - SVM): 0.13131082145488732
Recall (Test - SVM): 0.6015597718542661
F1 Score (Test - SVM): 0.215566864102778
ROC AUC Score (Test - SVM): 0.6812774706818494


# Compute ROC curve for test set for SVM
fpr_test_svm, tpr_test_svm, _ = roc_curve(y_test, y_test_prob_svm)

# Plot ROC curve for both training and test sets for SVM
plt.figure(figsize=(10, 5))
plt.plot(fpr_train_svm, tpr_train_svm, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train_svm:.2f})')
plt.plot(fpr_test_svm, tpr_test_svm, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test_svm:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve - SVM')
plt.legend()
plt.show()


import lime
from lime import lime_tabular
import random


# Initialize LIME explainer
explainer = lime_tabular.LimeTabularExplainer(X_train_sampled, mode='classification', feature_names=X_train.columns)

# Select 3 random instances for interpretation
num_instances = 3
random_indices = random.sample(range(len(X_train_sampled)), num_instances)
instances_to_interpret = X_train_sampled[random_indices]


# Convert instances to numpy array
instances_to_interpret = np.array(instances_to_interpret)

# Store local feature importance for each instance
local_feature_importance_list = []


# Calculate LIME explanations for each instance
for i, instance in enumerate(instances_to_interpret):
    # Explain the prediction
    explanation = explainer.explain_instance(instance, final_svm_model.predict_proba, num_features=len(X_train.columns))
    
    # Get local feature importance
    local_feature_importance = explanation.as_list()
    
    # Store local feature importance
    local_feature_importance_list.append(local_feature_importance)
    
    # Print local feature importance for each instance
    print(f"Local feature importance for instance {i + 1}:")
    for feature, weight in local_feature_importance:
        feature_name = feature.split(" <= ")[0]
        print(f"  Feature: {feature_name}, Importance: {weight}")

Local feature importance for instance 1:
  Feature: REST_DYNAMIC_FDEP_1M, Importance: -0.14704638840144174
  Feature: REST_DYNAMIC_FDEP_3M, Importance: -0.1343561014634013
  Feature: LDEAL_GRACE_DAYS_PCT_MED, Importance: -0.128537518976211
  Feature: TURNOVER_DYNAMIC_IL_1M, Importance: -0.12534781358947433
  Feature: TURNOVER_CC, Importance: -0.12038296708124654
  Feature: TURNOVER_DYNAMIC_IL_3M, Importance: -0.09394090499906342
  Feature: REST_AVG_PAYM, Importance: 0.08660016197642273
  Feature: REST_DYNAMIC_PAYM_1M, Importance: 0.08104084346987353
  Feature: CR_PROD_CNT_CCFP, Importance: -0.0770686421301206
  Feature: TRANS_AMOUNT_TENDENCY3M > 0.95, Importance: 0.062753645021981
  Feature: PACK_301, Importance: -0.0626304207084737
  Feature: REST_DYNAMIC_SAVE_3M, Importance: -0.059846834410807105
  Feature: TURNOVER_DYNAMIC_CUR_1M > 0.26, Importance: 0.05425795603405833
  Feature: CLNT_SETUP_TENOR, Importance: 0.050496393916219476
  Feature: TURNOVER_DYNAMIC_CC_1M, Importance: -0.049842379012377
  Feature: CR_PROD_CNT_VCU, Importance: -0.0493855862261953
  Feature: TRANS_COUNT_ATM_PRC > 0.61, Importance: 0.045205082043697
  Feature: -0.27 < REST_AVG_CUR, Importance: 0.044276938051427926
  Feature: REST_DYNAMIC_IL_1M, Importance: -0.0430587859453054
  Feature: AGE_GROUP_Group 5, Importance: -0.04279117875755887
  Feature: TURNOVER_DYNAMIC_CC_3M, Importance: -0.04204834392195089
  Feature: -0.68 < REST_DYNAMIC_CUR_1M, Importance: 0.03762203899019968
  Feature: CR_PROD_CNT_CC, Importance: -0.035041104520155096
  Feature: CR_PROD_CNT_TOVR, Importance: -0.034870056474790724
  Feature: TURNOVER_PAYM, Importance: -0.03395871354887335
  Feature: PACK_104, Importance: -0.033850301141275886
  Feature: AGE_GROUP_Group 4, Importance: -0.033545763215867774
  Feature: REST_DYNAMIC_IL_3M, Importance: -0.03260713696437662
  Feature: CR_PROD_CNT_IL, Importance: -0.030521606245448484
  Feature: PACK_101, Importance: -0.03006706483002833
  Feature: TRANS_CNT_TENDENCY3M > 0.77, Importance: -0.02751241165506097
  Feature: CNT_TRAN_ATM_TENDENCY3M > 0.80, Importance: 0.027018138982351465
  Feature: TOTAL_PROD_CNT, Importance: -0.026445103339784413
  Feature: REST_DYNAMIC_CC_1M, Importance: -0.025560463404692972
  Feature: SUM_TRAN_ATM_TENDENCY3M > 1.03, Importance: -0.024048910288208446
  Feature: PACK_105, Importance: -0.023602784166806577
  Feature: CR_PROD_CNT_PIL, Importance: -0.02334951495356999
  Feature: PACK_O01, Importance: -0.02243341170398687
  Feature: TURNOVER_DYNAMIC_PAYM_3M, Importance: -0.018725893410317527
  Feature: -0.61 < AGE_GROUP_Group 3, Importance: 0.018304753226403927
  Feature: PACK_102, Importance: -0.01569936867155987
  Feature: REST_DYNAMIC_CC_3M, Importance: -0.013963631207409575
  Feature: PACK_107, Importance: -0.013516881676065457
  Feature: REST_DYNAMIC_PAYM_3M, Importance: 0.012806238292290906
  Feature: PACK_103, Importance: -0.011665778388627774
  Feature: TRANS_COUNT_NAS_PRC, Importance: -0.011329953237167366
  Feature: TURNOVER_DYNAMIC_PAYM_1M, Importance: 0.01126833693208732
  Feature: TRANS_COUNT_SUP_PRC, Importance: -0.010986207756181577
  Feature: AMOUNT_RUB_CLO_PRC, Importance: 0.009156705895887664
  Feature: -0.19 < total_turnover, Importance: -0.00903862769297212
  Feature: TURNOVER_DYNAMIC_CUR_3M > 0.97, Importance: 0.008842021310980677
  Feature: AMOUNT_RUB_SUP_PRC, Importance: -0.00879065079255652
  Feature: PACK_K01 > -0.55, Importance: -0.008051215081985141
  Feature: AMOUNT_RUB_ATM_PRC > 0.93, Importance: 0.0078060771537052575
  Feature: AGE_GROUP_Group 1, Importance: -0.007720580835343515
  Feature: AMOUNT_RUB_NAS_PRC, Importance: -0.005815733831187624
  Feature: REST_DYNAMIC_CUR_3M > 0.91, Importance: -0.004951308508316479
  Feature: AGE_GROUP_Group 2, Importance: 0.002430761940829926
  Feature: PACK_108, Importance: 0.0
  Feature: PACK_109, Importance: 0.0
  Feature: PACK_M01, Importance: 0.0
Local feature importance for instance 2:
  Feature: LDEAL_GRACE_DAYS_PCT_MED, Importance: -0.15033962639953805
  Feature: TURNOVER_DYNAMIC_IL_1M, Importance: -0.13162791320437056
  Feature: REST_DYNAMIC_FDEP_3M, Importance: -0.12209109931776727
  Feature: TURNOVER_CC, Importance: -0.1181814835312058
  Feature: REST_DYNAMIC_CUR_1M > 0.24, Importance: -0.11175606909627735
  Feature: REST_DYNAMIC_FDEP_1M, Importance: -0.1067421436640174
  Feature: REST_AVG_PAYM, Importance: 0.09413727496114302
  Feature: TURNOVER_DYNAMIC_CC_1M, Importance: -0.08513845392246741
  Feature: TURNOVER_DYNAMIC_IL_3M, Importance: -0.07879574593275772
  Feature: CR_PROD_CNT_VCU, Importance: -0.07605456474547337
  Feature: REST_DYNAMIC_PAYM_1M, Importance: 0.07491733355085692
  Feature: TRANS_AMOUNT_TENDENCY3M > 0.95, Importance: 0.06960093640113842
  Feature: AGE_GROUP_Group 5, Importance: -0.061405751604960576
  Feature: REST_DYNAMIC_SAVE_3M, Importance: -0.06055926129782365
  Feature: REST_DYNAMIC_CC_1M, Importance: -0.060404267234258686
  Feature: TURNOVER_DYNAMIC_CUR_1M > 0.26, Importance: 0.05973097568919207
  Feature: REST_AVG_CUR, Importance: 0.059293602441898415
  Feature: CR_PROD_CNT_CCFP, Importance: -0.057775266630382106
  Feature: PACK_104, Importance: -0.054858303529103335
  Feature: PACK_301, Importance: -0.05452163816277369
  Feature: REST_DYNAMIC_IL_1M, Importance: -0.049760705020195646
  Feature: CR_PROD_CNT_TOVR, Importance: -0.04849033889735181
  Feature: TRANS_CNT_TENDENCY3M > 0.77, Importance: -0.03697573423330415
  Feature: -0.88 < CLNT_SETUP_TENOR, Importance: 0.03565213994941347
  Feature: CR_PROD_CNT_IL, Importance: -0.03450365483177571
  Feature: CR_PROD_CNT_PIL, Importance: -0.03269968022081783
  Feature: TOTAL_PROD_CNT, Importance: -0.031946135899510515
  Feature: CNT_TRAN_ATM_TENDENCY3M > 0.80, Importance: 0.03140530455953711
  Feature: TURNOVER_PAYM, Importance: -0.030461971546487544
  Feature: SUM_TRAN_ATM_TENDENCY3M > 1.03, Importance: -0.028745886982190702
  Feature: REST_DYNAMIC_CC_3M, Importance: -0.027742188798718693
  Feature: PACK_101, Importance: -0.024733199307204797
  Feature: TURNOVER_DYNAMIC_PAYM_3M, Importance: -0.02143200613060704
  Feature: -0.67 < PACK_102, Importance: 0.019538779413642775
  Feature: AMOUNT_RUB_CLO_PRC, Importance: 0.018906796582977704
  Feature: REST_DYNAMIC_IL_3M, Importance: -0.018181765666110246
  Feature: CR_PROD_CNT_CC, Importance: -0.016856014358800037
  Feature: AGE_GROUP_Group 3, Importance: -0.01668374828399379
  Feature: PACK_105, Importance: -0.016095334248290762
  Feature: AGE_GROUP_Group 4, Importance: -0.015354812188503283
  Feature: PACK_O01, Importance: -0.015040748735270566
  Feature: TRANS_COUNT_NAS_PRC, Importance: -0.012957698089780464
  Feature: AMOUNT_RUB_NAS_PRC, Importance: -0.011919845347396266
  Feature: -0.19 < total_turnover, Importance: -0.011408083618180919
  Feature: PACK_103, Importance: -0.011005188551739329
  Feature: TRANS_COUNT_SUP_PRC, Importance: -0.010266928591673125
  Feature: REST_DYNAMIC_PAYM_3M, Importance: 0.009813522843497855
  Feature: TURNOVER_DYNAMIC_CC_3M, Importance: -0.009020165792229867
  Feature: PACK_K01, Importance: 0.00844230707037991
  Feature: -0.75 < AMOUNT_RUB_ATM_PRC, Importance: -0.007046573174242423
  Feature: AMOUNT_RUB_SUP_PRC, Importance: 0.0057512658587970085
  Feature: PACK_107, Importance: -0.005455849179279083
  Feature: -0.15 < TRANS_COUNT_ATM_PRC, Importance: 0.004573793532338947
  Feature: TURNOVER_DYNAMIC_CUR_3M > 0.97, Importance: 0.004270413539247264
  Feature: TURNOVER_DYNAMIC_PAYM_1M, Importance: 0.0035055109408266235
  Feature: -1.09 < AGE_GROUP_Group 2, Importance: -0.0028267596092826906
  Feature: REST_DYNAMIC_CUR_3M > 0.91, Importance: -0.001372786973392137
  Feature: AGE_GROUP_Group 1, Importance: -0.0010534799595602927
  Feature: PACK_M01, Importance: 0.0
  Feature: PACK_108, Importance: 0.0
  Feature: PACK_109, Importance: 0.0
Local feature importance for instance 3:
  Feature: LDEAL_GRACE_DAYS_PCT_MED, Importance: -0.18273374408271564
  Feature: REST_DYNAMIC_FDEP_1M, Importance: -0.15247721830109842
  Feature: TURNOVER_DYNAMIC_CC_1M, Importance: -0.15007838957166184
  Feature: TURNOVER_DYNAMIC_IL_1M, Importance: -0.1340305217061444
  Feature: TURNOVER_CC, Importance: -0.1252740006590734
  Feature: REST_DYNAMIC_FDEP_3M, Importance: -0.12247812900900056
  Feature: AGE_GROUP_Group 5, Importance: -0.1170846831336262
  Feature: TURNOVER_DYNAMIC_IL_3M, Importance: -0.11127853178152769
  Feature: REST_AVG_PAYM > -0.16, Importance: -0.09388590232178452
  Feature: CR_PROD_CNT_VCU, Importance: -0.08681019637887757
  Feature: REST_DYNAMIC_CUR_1M, Importance: 0.08066765418119314
  Feature: REST_DYNAMIC_PAYM_1M > -0.29, Importance: -0.07128051725401972
  Feature: CR_PROD_CNT_CCFP, Importance: -0.06631842572320816
  Feature: REST_DYNAMIC_SAVE_3M, Importance: -0.06382160983065908
  Feature: REST_AVG_CUR, Importance: 0.058925603135970804
  Feature: PACK_104, Importance: -0.05803740348369156
  Feature: TRANS_COUNT_SUP_PRC > 0.53, Importance: 0.0493869118647884
  Feature: REST_DYNAMIC_IL_3M, Importance: -0.04675463404818441
  Feature: TURNOVER_DYNAMIC_CC_3M, Importance: -0.04565866952598865
  Feature: PACK_301, Importance: -0.04152068067444318
  Feature: REST_DYNAMIC_CC_3M, Importance: -0.04015092895731365
  Feature: CR_PROD_CNT_TOVR, Importance: -0.03673529812106345
  Feature: TURNOVER_PAYM > -0.19, Importance: 0.03370349033155072
  Feature: total_turnover > -0.19, Importance: 0.032315697514408215
  Feature: REST_DYNAMIC_CC_1M, Importance: -0.030857073495342422
  Feature: AGE_GROUP_Group 4, Importance: -0.03050067755754937
  Feature: CR_PROD_CNT_IL, Importance: -0.02784785164336459
  Feature: TURNOVER_DYNAMIC_CUR_1M, Importance: -0.027460786032147747
  Feature: TOTAL_PROD_CNT, Importance: -0.027037710216433575
  Feature: REST_DYNAMIC_IL_1M, Importance: -0.0249776956129783
  Feature: CR_PROD_CNT_PIL, Importance: -0.024814933330928164
  Feature: CR_PROD_CNT_CC, Importance: -0.02395966963025233
  Feature: TRANS_COUNT_ATM_PRC, Importance: -0.023703950629053713
  Feature: AMOUNT_RUB_CLO_PRC > -0.15, Importance: -0.022461252823012482
  Feature: PACK_105 > -0.42, Importance: 0.02144722103133847
  Feature: AGE_GROUP_Group 3, Importance: -0.01824763788508651
  Feature: PACK_103, Importance: -0.017616403428768183
  Feature: TURNOVER_DYNAMIC_PAYM_3M > -0.39, Importance: 0.016606445745080942
  Feature: -0.07 < TRANS_AMOUNT_TENDENCY3M, Importance: 0.015536407473162435
  Feature: PACK_101, Importance: -0.015198224966769647
  Feature: PACK_K01, Importance: 0.013833835672309017
  Feature: REST_DYNAMIC_PAYM_3M > -0.41, Importance: -0.013120603181323976
  Feature: -0.07 < TRANS_CNT_TENDENCY3M, Importance: -0.012527493299462861
  Feature: REST_DYNAMIC_CUR_3M, Importance: 0.012300638932376417
  Feature: PACK_O01, Importance: -0.011530029344972046
  Feature: TURNOVER_DYNAMIC_CUR_3M, Importance: -0.011511048620943327
  Feature: TRANS_COUNT_NAS_PRC, Importance: -0.01123294967664602
  Feature: AMOUNT_RUB_NAS_PRC, Importance: -0.010371804849121369
  Feature: AMOUNT_RUB_ATM_PRC, Importance: -0.009909888561614328
  Feature: PACK_102, Importance: -0.00949138013166266
  Feature: -0.22 < CLNT_SETUP_TENOR, Importance: -0.008450207614140896
  Feature: AGE_GROUP_Group 1, Importance: -0.007611115016231038
  Feature: PACK_107, Importance: -0.006491310042647481
  Feature: TURNOVER_DYNAMIC_PAYM_1M > -0.27, Importance: -0.004371326696409258
  Feature: -0.49 < SUM_TRAN_ATM_TENDENCY3M, Importance: -0.0011512691169009409
  Feature: AMOUNT_RUB_SUP_PRC > 0.05, Importance: -0.0007911555254184891
  Feature: -1.09 < AGE_GROUP_Group 2, Importance: 0.000412829614688812
  Feature: -0.60 < CNT_TRAN_ATM_TENDENCY3M, Importance: -0.00012432112300862357
  Feature: PACK_M01, Importance: 0.0
  Feature: PACK_108, Importance: 0.0
  Feature: PACK_109, Importance: 0.0


# Visualize local feature importance
for i, local_feature_importance in enumerate(local_feature_importance_list):
    plt.figure(figsize=(20, 20))
    features = [feature.split(" <= ")[0] for feature, _ in local_feature_importance]
    weights = [weight for _, weight in local_feature_importance]
    plt.barh(features, weights, color='skyblue')
    plt.xlabel('Importance')
    plt.ylabel('Feature')
    plt.title(f'Local Feature Importance for Instance {i+1}')
    plt.gca().invert_yaxis()  # Invert y-axis to have the most important feature at the top
    plt.show()


# Select one instance for interpretation
instance_to_interpret = X_train_sampled[0]

# Store global feature importance
global_feature_importance = np.zeros(X_train_sampled.shape[1])


# Calculate LIME explanations for each feature
for feature_index in range(len(X_train.columns)):
    # Perturb the current feature
    instance_perturbed = np.array(instance_to_interpret)
    instance_perturbed[feature_index] = np.random.choice(X_train_sampled[:, feature_index])

    # Explain the prediction for the perturbed instance
    explanation = explainer.explain_instance(instance_perturbed, final_svm_model.predict_proba, num_features=len(X_train.columns))

    # Get the weights for each feature
    weights = explanation.local_exp[1]

    # Aggregate the weights for the current feature
    global_feature_importance[feature_index] = np.mean([weight for _, weight in weights])


# Sort features by importance
sorted_indices = np.argsort(global_feature_importance)[::-1]

# Print global feature importance
print("\nGlobal feature importance:")
for idx in sorted_indices:
    print(f"Feature: {X_train.columns[idx]}, Importance: {global_feature_importance[idx]}")

Global feature importance:
Feature: TURNOVER_DYNAMIC_PAYM_3M, Importance: -0.02402635451036813
Feature: TRANS_COUNT_ATM_PRC, Importance: -0.024085165851705306
Feature: PACK_107, Importance: -0.0242412017054293
Feature: AMOUNT_RUB_SUP_PRC, Importance: -0.02496658279819607
Feature: TOTAL_PROD_CNT, Importance: -0.025601312618376088
Feature: PACK_103, Importance: -0.026088222683597853
Feature: PACK_105, Importance: -0.02620470391843965
Feature: REST_DYNAMIC_SAVE_3M, Importance: -0.026284698433760672
Feature: TRANS_COUNT_NAS_PRC, Importance: -0.02653705871451709
Feature: TURNOVER_DYNAMIC_IL_3M, Importance: -0.026635751337777673
Feature: REST_DYNAMIC_CC_3M, Importance: -0.02670042056654317
Feature: LDEAL_GRACE_DAYS_PCT_MED, Importance: -0.026782281274570965
Feature: PACK_301, Importance: -0.0267929318519944
Feature: REST_AVG_CUR, Importance: -0.026827069687588103
Feature: PACK_108, Importance: -0.02700023351233272
Feature: REST_DYNAMIC_PAYM_3M, Importance: -0.02700478565935417
Feature: REST_DYNAMIC_IL_3M, Importance: -0.027055387571754796
Feature: CR_PROD_CNT_PIL, Importance: -0.02706837782232121
Feature: PACK_101, Importance: -0.02709359051498582
Feature: REST_DYNAMIC_CUR_3M, Importance: -0.02712206503117224
Feature: PACK_102, Importance: -0.027268521243739675
Feature: TURNOVER_DYNAMIC_CC_3M, Importance: -0.027276029325054258
Feature: TRANS_AMOUNT_TENDENCY3M, Importance: -0.02733801445210523
Feature: SUM_TRAN_ATM_TENDENCY3M, Importance: -0.02736884684856256
Feature: TURNOVER_CC, Importance: -0.02744184629552572
Feature: CNT_TRAN_ATM_TENDENCY3M, Importance: -0.027461692809109824
Feature: REST_DYNAMIC_IL_1M, Importance: -0.027535723900900824
Feature: CR_PROD_CNT_VCU, Importance: -0.027540784412696483
Feature: TURNOVER_PAYM, Importance: -0.02756353909292793
Feature: TURNOVER_DYNAMIC_IL_1M, Importance: -0.027572678146996772
Feature: REST_DYNAMIC_PAYM_1M, Importance: -0.027620890557878364
Feature: AMOUNT_RUB_ATM_PRC, Importance: -0.0276965140593232
Feature: REST_DYNAMIC_FDEP_3M, Importance: -0.02772631636544595
Feature: TURNOVER_DYNAMIC_PAYM_1M, Importance: -0.027734178130707388
Feature: CLNT_SETUP_TENOR, Importance: -0.027755874118872863
Feature: TRANS_CNT_TENDENCY3M, Importance: -0.027760441834269452
Feature: REST_DYNAMIC_CC_1M, Importance: -0.02778434017687035
Feature: AGE_GROUP_Group 2, Importance: -0.027946610390325165
Feature: TURNOVER_DYNAMIC_CUR_3M, Importance: -0.027985174525529573
Feature: PACK_104, Importance: -0.02800479027846567
Feature: REST_DYNAMIC_FDEP_1M, Importance: -0.028047703919529928
Feature: PACK_109, Importance: -0.028152856960064792
Feature: PACK_M01, Importance: -0.028263372986734696
Feature: TRANS_COUNT_SUP_PRC, Importance: -0.02837634877554955
Feature: REST_DYNAMIC_CUR_1M, Importance: -0.028383292625548198
Feature: REST_AVG_PAYM, Importance: -0.028433052122288045
Feature: CR_PROD_CNT_CCFP, Importance: -0.028437276056741304
Feature: PACK_K01, Importance: -0.0284771983342934
Feature: CR_PROD_CNT_TOVR, Importance: -0.02853707755464891
Feature: AGE_GROUP_Group 3, Importance: -0.028539934459198127
Feature: TURNOVER_DYNAMIC_CUR_1M, Importance: -0.028544731302237936
Feature: CR_PROD_CNT_IL, Importance: -0.028559031766838685
Feature: TURNOVER_DYNAMIC_CC_1M, Importance: -0.028613844251757205
Feature: AMOUNT_RUB_CLO_PRC, Importance: -0.029002905924930548
Feature: CR_PROD_CNT_CC, Importance: -0.029090809399044893
Feature: AGE_GROUP_Group 1, Importance: -0.02913077530645805
Feature: AGE_GROUP_Group 4, Importance: -0.02931063140112495
Feature: PACK_O01, Importance: -0.029380184020841617
Feature: total_turnover, Importance: -0.029806336722668993
Feature: AGE_GROUP_Group 5, Importance: -0.030359096123964237
Feature: AMOUNT_RUB_NAS_PRC, Importance: -0.030755949369092667


# Print names of top 10 features with importance
num_top_features = 10
top_feature_indices = sorted_indices[:num_top_features]
top_feature_names = X_train.columns[top_feature_indices]
top_feature_importance = global_feature_importance[top_feature_indices]

print("Top 10 Features and Their Importance:")
for feature_name, importance in zip(top_feature_names, top_feature_importance):
    print(f"Feature: {feature_name}, Importance: {importance}")

Top 10 Features and Their Importance:
Feature: TURNOVER_DYNAMIC_PAYM_3M, Importance: -0.02402635451036813
Feature: TRANS_COUNT_ATM_PRC, Importance: -0.024085165851705306
Feature: PACK_107, Importance: -0.0242412017054293
Feature: AMOUNT_RUB_SUP_PRC, Importance: -0.02496658279819607
Feature: TOTAL_PROD_CNT, Importance: -0.025601312618376088
Feature: PACK_103, Importance: -0.026088222683597853
Feature: PACK_105, Importance: -0.02620470391843965
Feature: REST_DYNAMIC_SAVE_3M, Importance: -0.026284698433760672
Feature: TRANS_COUNT_NAS_PRC, Importance: -0.02653705871451709
Feature: TURNOVER_DYNAMIC_IL_3M, Importance: -0.026635751337777673


# Select the top 10 features
top_feature_indices = sorted_indices[:10]
X_train_top_features = X_train.iloc[:, top_feature_indices]
X_test_top_features = X_test.iloc[:, top_feature_indices]


X_train_undersampled, y_train_undersampled = perform_random_undersampling(X_train_top_features, y_train)

# Initialize the StandardScaler object
scaler = StandardScaler()

# Fit and transform the training features
X_train_scaled = scaler.fit_transform(X_train_undersampled)

# Transform the test features using the parameters learned from the training data
X_test_scaled = scaler.transform(X_test_top_features)


# Sample 1000 instances from the training data
sample_size = 1000
X_train_sampled = X_train_undersampled.sample(n=sample_size, random_state=42)
y_train_sampled = y_train_undersampled.sample(n=sample_size, random_state=42)

# Train an SVM model
svm_model = SVC(kernel='linear', random_state=42)
svm_model.fit(X_train_sampled, y_train_sampled)

# Evaluate the model on the training data
train_accuracy = svm_model.score(X_train_sampled, y_train_sampled)
print(f"Train Accuracy: {train_accuracy:.2f}")

# Predict labels for the test data
y_pred_test = svm_model.predict(X_test_top_features)

# Evaluate the model on the test data
test_accuracy = accuracy_score(y_test, y_pred_test)
print(f"Test Accuracy: {test_accuracy:.2f}")

Train Accuracy: 0.60
Test Accuracy: 0.66


y_pred_train = svm_model.predict(X_train_sampled)
train_precision = precision_score(y_train_sampled, y_pred_train)
train_recall = recall_score(y_train_sampled, y_pred_train)
train_f1 = f1_score(y_train_sampled, y_pred_train)

print("Training Metrics:")
print(f"Precision: {train_precision:.2f}")
print(f"Recall: {train_recall:.2f}")
print(f"F1-score: {train_f1:.2f}")

# Precision, Recall, and F1-score for Testing Data
test_precision = precision_score(y_test, y_pred_test)
test_recall = recall_score(y_test, y_pred_test)
test_f1 = f1_score(y_test, y_pred_test)

print("\nTesting Metrics:")
print(f"Precision: {test_precision:.2f}")
print(f"Recall: {test_recall:.2f}")
print(f"F1-score: {test_f1:.2f}")

# ROC Curve for Training Data
train_y_scores = svm_model.decision_function(X_train_sampled)
train_fpr, train_tpr, train_thresholds = roc_curve(y_train_sampled, train_y_scores)
train_auc = roc_auc_score(y_train_sampled, train_y_scores)

# ROC Curve for Testing Data
test_y_scores = svm_model.decision_function(X_test_top_features)
test_fpr, test_tpr, test_thresholds = roc_curve(y_test, test_y_scores)
test_auc = roc_auc_score(y_test, test_y_scores)

# Plot ROC Curves
plt.figure(figsize=(10, 6))
plt.plot(train_fpr, train_tpr, "b:", linewidth=2, label="Train ROC Curve (AUC = {:.2f})".format(train_auc))
plt.plot(test_fpr, test_tpr, "r-", linewidth=2, label="Test ROC Curve (AUC = {:.2f})".format(test_auc))
plt.plot([0, 1], [0, 1], 'k--')  # Dashed diagonal
plt.legend(loc="lower right", fontsize=14)
plt.xlabel("False Positive Rate (FPR)", fontsize=14)
plt.ylabel("True Positive Rate (TPR)", fontsize=14)
plt.title("ROC Curve", fontsize=16)
plt.grid(True)
plt.show()

Training Metrics:
Precision: 0.66
Recall: 0.51
F1-score: 0.57

Testing Metrics:
Precision: 0.12
Recall: 0.50
F1-score: 0.19


X_train_undersampled, y_train_undersampled = perform_random_undersampling(X_train, y_train)
# Initialize the StandardScaler object
scaler = StandardScaler()
# Fit and transform the training features
X_train_scaled = scaler.fit_transform(X_train_undersampled)
# Transform the test features using the parameters learned from the training data
X_test_scaled = scaler.transform(X_test)
# Specify the desired sample size
sample_size = 100  # Adjust this according to your requirements
# Randomly sample the training data
X_train_sampled, y_train_sampled = resample(X_train_scaled, y_train_undersampled, n_samples=sample_size, random_state=42)
# Initialize the Support Vector Classifier
svm = SVC(probability=True)


import shap

# Define a function to convert SVC model into a black-box model
def f(X):
    return final_svm_model.predict_proba(X)[:, 1]

# Summarize the background data using shap.sample or shap.kmeans
background_summary = shap.sample(X_train_sampled[:30], 30)  # Adjust the number of samples as needed

# Initialize SHAP kernel explainer with the black-box model function and summarized background data
explainer = shap.KernelExplainer(f, background_summary)

# Calculate SHAP values for a set of instances
shap_values = explainer.shap_values(X_train_sampled[:3])

# Plot local explanations for the first three instances
shap.initjs()
shap.force_plot(explainer.expected_value, shap_values, X_train_sampled[:3])

# Calculate SHAP values for the entire training dataset
shap_values_all = explainer.shap_values(X_train_sampled)

# Summary plot for global feature importance
shap.summary_plot(shap_values_all, X_train_sampled)

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# Define a threshold for SHAP values
threshold = 0.001  # Adjust this threshold as needed

# Find features with SHAP values above the threshold
above_threshold_features = np.where(np.abs(shap_values_all).mean(0) > threshold)[0]

# Check if any features are selected
if len(above_threshold_features) == 0:
    print("No features found above the threshold. Try lowering the threshold.")
else:
    # Extract the features above the threshold
    X_train_selected = X_train_sampled[:, above_threshold_features]

    # Split the data into training and testing sets
    X_train_selected, X_test_selected, y_train, y_test = train_test_split(X_train_selected, y_train_sampled, test_size=0.2, random_state=42)

    # Train a new model on the selected features
    new_model = SVC(probability=True)  # You can change the model if desired
    new_model.fit(X_train_selected, y_train)

    # Evaluate the new model
    accuracy = new_model.score(X_test_selected, y_test)
    print("Accuracy of the new model on the test set:", accuracy)

Accuracy of the new model on the test set: 0.75


# Train a new model on the selected features
new_model = SVC(probability=True)  # You can change the model if desired
new_model.fit(X_train_selected, y_train)

# Make predictions on training and testing data
y_train_pred = new_model.predict(X_train_selected)
y_test_pred = new_model.predict(X_test_selected)

# Calculate evaluation metrics for training set
train_accuracy = accuracy_score(y_train, y_train_pred)
train_precision = precision_score(y_train, y_train_pred)
train_recall = recall_score(y_train, y_train_pred)
train_f1 = f1_score(y_train, y_train_pred)
train_roc_auc = roc_auc_score(y_train, new_model.predict_proba(X_train_selected)[:, 1])

# Calculate evaluation metrics for test set
test_accuracy = accuracy_score(y_test, y_test_pred)
test_precision = precision_score(y_test, y_test_pred)
test_recall = recall_score(y_test, y_test_pred)
test_f1 = f1_score(y_test, y_test_pred)
test_roc_auc = roc_auc_score(y_test, new_model.predict_proba(X_test_selected)[:, 1])

print("Training Set Metrics:")
print("Accuracy:", train_accuracy)
print("Precision:", train_precision)
print("Recall:", train_recall)
print("F1 Score:", train_f1)
print("ROC AUC Score:", train_roc_auc)

print("\nTest Set Metrics:")
print("Accuracy:", test_accuracy)
print("Precision:", test_precision)
print("Recall:", test_recall)
print("F1 Score:", test_f1)
print("ROC AUC Score:", test_roc_auc)

# ROC Curve for training set
fpr_train, tpr_train, thresholds_train = roc_curve(y_train, new_model.predict_proba(X_train_selected)[:, 1])

# ROC Curve for test set
fpr_test, tpr_test, thresholds_test = roc_curve(y_test, new_model.predict_proba(X_test_selected)[:, 1])

Training Set Metrics:
Accuracy: 0.8625
Precision: 0.9310344827586207
Recall: 0.75
F1 Score: 0.8307692307692308
ROC AUC Score: 0.9494949494949496

Test Set Metrics:
Accuracy: 0.75
Precision: 0.75
Recall: 0.42857142857142855
F1 Score: 0.5454545454545454
ROC AUC Score: 0.8131868131868132


# Plot ROC curve for both training and test sets
plt.figure(figsize=(8, 6))
plt.plot(fpr_train, tpr_train, label='ROC Curve (Train)')
plt.plot(fpr_test, tpr_test, label='ROC Curve (Test)')
plt.plot([0, 1], [0, 1], 'k--', label='Random Guessing')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.grid(True)
plt.show()


## Decision Tree


# Target variable
t = df['TARGET']
  
# Features
f = df.drop(columns=['TARGET']) 
f_train, f_test, t_train, t_test = train_test_split(f, t, test_size=0.3, random_state=100)


print(f_train.shape)
print(f_test.shape)
print(t_train.shape)
print(t_test.shape)

(248633, 61)
(106557, 61)
(248633,)
(106557,)


print(t_train.groupby(t_train).count())

TARGET
0    228299
1     20334
Name: TARGET, dtype: int64


f_train_undersampled,t_train_undersampled = perform_random_undersampling(f_train,t_train)


f_train_undersampled.shape

(40668, 61)


t_train_undersampled.shape

(40668,)


t_train_undersampled.value_counts()

TARGET
0    20334
1    20334
Name: count, dtype: int64


scaler = StandardScaler()

f_train_scaled = scaler.fit_transform(f_train_undersampled)

f_test_scaled = scaler.transform(f_test)


decision_tree = DecisionTreeClassifier()
decision_tree.fit(f_train_scaled, t_train_undersampled)

# Predictions on the training set using the final Decision Tree model
y_train_pred_dt = decision_tree.predict(f_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob_dt = decision_tree.predict_proba(f_train_scaled)[:, 1]

# Compute the confusion matrix on the training set for Decision Tree
cm_train_dt = confusion_matrix(t_train_undersampled, y_train_pred_dt)

# Compute accuracy, precision, recall, and F1 score on the training set for Decision Tree
accuracy_train_dt = (cm_train_dt[0, 0] + cm_train_dt[1, 1]) / np.sum(cm_train_dt)
precision_train_dt = precision_score(t_train_undersampled, y_train_pred_dt)
recall_train_dt = recall_score(t_train_undersampled, y_train_pred_dt)
f1_train_dt = f1_score(t_train_undersampled, y_train_pred_dt)

# Compute the ROC AUC score on the training set for Decision Tree
roc_auc_train_dt = roc_auc_score(t_train_undersampled, y_train_prob_dt)

# Compute ROC curve for training set for Decision Tree
fpr_train_dt, tpr_train_dt, _ = roc_curve(t_train_undersampled, y_train_prob_dt)

print("Confusion Matrix (Training Set - Decision Tree):\n", cm_train_dt)
print("Accuracy (Train - Decision Tree):", accuracy_train_dt)
print("Precision (Train - Decision Tree):", precision_train_dt)
print("Recall (Train - Decision Tree):", recall_train_dt)
print("F1 Score (Train - Decision Tree):", f1_train_dt)
print("ROC AUC Score (Train - Decision Tree):", roc_auc_train_dt)

Confusion Matrix (Training Set - Decision Tree):
 [[20334     0]
 [    5 20329]]
Accuracy (Train - Decision Tree): 0.9998770532113701
Precision (Train - Decision Tree): 1.0
Recall (Train - Decision Tree): 0.9997541064227402
F1 Score (Train - Decision Tree): 0.9998770380935986
ROC AUC Score (Train - Decision Tree): 0.9999999685589013


X_test_scaled.shape

(106557, 61)


y_test.shape

(20,)


# Predictions on the test set using the final Decision Tree model
y_test_pred_dt = decision_tree.predict(f_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob_dt = decision_tree.predict_proba(f_test_scaled)[:, 1]

# Compute the confusion matrix on the test set for Decision Tree
cm_test_dt = confusion_matrix(t_test, y_test_pred_dt)

# Compute accuracy, precision, recall, and F1 score on the test set for Decision Tree
accuracy_test_dt = (cm_test_dt[0, 0] + cm_test_dt[1, 1]) / np.sum(cm_test_dt)
precision_test_dt = precision_score(t_test, y_test_pred_dt)
recall_test_dt = recall_score(t_test, y_test_pred_dt)
f1_test_dt = f1_score(t_test, y_test_pred_dt)

# Compute the ROC AUC score on the test set for Decision Tree
roc_auc_test_dt = roc_auc_score(t_test, y_test_prob_dt)

print("Confusion Matrix (Test Set - Decision Tree):\n", cm_test_dt)
print("Accuracy (Test - Decision Tree):", accuracy_test_dt)
print("Precision (Test - Decision Tree):", precision_test_dt)
print("Recall (Test - Decision Tree):", recall_test_dt)
print("F1 Score (Test - Decision Tree):", f1_test_dt)
print("ROC AUC Score (Test - Decision Tree):", roc_auc_test_dt)

Confusion Matrix (Test Set - Decision Tree):
 [[66474 31492]
 [ 2869  5722]]
Accuracy (Test - Decision Tree): 0.6775340897360098
Precision (Test - Decision Tree): 0.1537593378835922
Recall (Test - Decision Tree): 0.6660458619485508
F1 Score (Test - Decision Tree): 0.24984172033620783
ROC AUC Score (Test - Decision Tree): 0.6723178462854968


# Compute ROC curve for test set for Decision Tree
fpr_test_dt, tpr_test_dt, _ = roc_curve(t_test, y_test_prob_dt)

# Plot ROC curve for both training and test sets for Decision Tree
plt.figure(figsize=(10, 5))
plt.plot(fpr_train_dt, tpr_train_dt, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train_dt:.2f})')
plt.plot(fpr_test_dt, tpr_test_dt, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test_dt:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve - Decision Tree')
plt.legend()
plt.show()


## Random Forest


rf =  RandomForestClassifier(n_estimators=100)
rf.fit(f_train_scaled, t_train_undersampled)

# Predictions on the training set using the final Random Forest model
y_train_pred_rf = rf.predict(f_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob_rf = rf.predict_proba(f_train_scaled)[:, 1]

# Compute the confusion matrix on the training set for Random Forest
cm_train_rf = confusion_matrix(t_train_undersampled, y_train_pred_rf)

# Compute accuracy, precision, recall, and F1 score on the training set for Random Forest
accuracy_train_rf = (cm_train_rf[0, 0] + cm_train_rf[1, 1]) / np.sum(cm_train_rf)
precision_train_rf = precision_score(t_train_undersampled, y_train_pred_rf)
recall_train_rf = recall_score(t_train_undersampled, y_train_pred_rf)
f1_train_rf = f1_score(t_train_undersampled, y_train_pred_rf)

# Compute the ROC AUC score on the training set for Random Forest
roc_auc_train_rf = roc_auc_score(t_train_undersampled, y_train_prob_rf)

# Compute ROC curve for training set for Random Forest
fpr_train_rf, tpr_train_rf, _ = roc_curve(t_train_undersampled, y_train_prob_rf)

print("Confusion Matrix (Training Set - Random Forest):\n", cm_train_rf)
print("Accuracy (Train - Random Forest):", accuracy_train_rf)
print("Precision (Train - Random Forest):", precision_train_rf)
print("Recall (Train - Random Forest):", recall_train_rf)
print("F1 Score (Train - Random Forest):", f1_train_rf)
print("ROC AUC Score (Train - Random Forest):", roc_auc_train_rf)

Confusion Matrix (Training Set - Random Forest):
 [[20331     3]
 [    2 20332]]
Accuracy (Train - Random Forest): 0.9998770532113701
Precision (Train - Random Forest): 0.9998524711089255
Recall (Train - Random Forest): 0.9999016425690961
F1 Score (Train - Random Forest): 0.9998770562344783
ROC AUC Score (Train - Random Forest): 0.9999985488723678


# Predictions on the test set using the final Random Forest model
y_test_pred_rf = decision_tree.predict(f_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob_rf = decision_tree.predict_proba(f_test_scaled)[:, 1]

# Compute the confusion matrix on the test set for Random Forest
cm_test_rf = confusion_matrix(t_test, y_test_pred_rf)

# Compute accuracy, precision, recall, and F1 score on the test set for Random Forest
accuracy_test_rf = (cm_test_rf[0, 0] + cm_test_rf[1, 1]) / np.sum(cm_test_rf)
precision_test_rf = precision_score(t_test, y_test_pred_rf)
recall_test_rf = recall_score(t_test, y_test_pred_rf)
f1_test_rf = f1_score(t_test, y_test_pred_rf)

# Compute the ROC AUC score on the test set for Random Forest
roc_auc_test_rf = roc_auc_score(t_test, y_test_prob_rf)

print("Confusion Matrix (Test Set - Random Forest):\n", cm_test_rf)
print("Accuracy (Test - Random Forest):", accuracy_test_rf)
print("Precision (Test - Random Forest):", precision_test_rf)
print("Recall (Test - Random Forest):", recall_test_rf)
print("F1 Score (Test - Random Forest):", f1_test_rf)
print("ROC AUC Score (Test - Random Forest):", roc_auc_test_rf)

Confusion Matrix (Test Set - Random Forest):
 [[66474 31492]
 [ 2869  5722]]
Accuracy (Test - Random Forest): 0.6775340897360098
Precision (Test - Random Forest): 0.1537593378835922
Recall (Test - Random Forest): 0.6660458619485508
F1 Score (Test - Random Forest): 0.24984172033620783
ROC AUC Score (Test - Random Forest): 0.6723178462854968


# Compute ROC curve for test set for Random Forest
fpr_test_rf, tpr_test_rf, _ = roc_curve(t_test, y_test_prob_rf)

# Plot ROC curve for both training and test sets for Random Forest
plt.figure(figsize=(10, 5))
plt.plot(fpr_train_dt, tpr_train_dt, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train_rf:.2f})')
plt.plot(fpr_test_dt, tpr_test_dt, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test_rf:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve - Random Forest')
plt.legend()
plt.show()


#Feature importance for Decision Tree


coefficients_dt = decision_tree.feature_importances_[0]

# Create a DataFrame to store feature names and their coefficients
feature_coefficients_dt = pd.DataFrame({'Feature': X_train.columns, 'Coefficient': coefficients})

# Sort the DataFrame by absolute coefficient values to identify the most important features
feature_coefficients_dt['Absolute Coefficient'] = np.abs(feature_coefficients_dt['Coefficient'])
feature_coefficients_dt = feature_coefficients_dt.sort_values(by='Absolute Coefficient', ascending=False)

# Print the top features
print("Top features:")
print(feature_coefficients_dt.head(10))  # Adjust the number of top features as needed

# Plot feature coefficients
plt.figure(figsize=(20, 16))
plt.barh(feature_coefficients_dt['Feature'], feature_coefficients_dt['Coefficient'])
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Feature Coefficients')
plt.show()

Top features:
                     Feature  Coefficient  Absolute Coefficient
7               REST_AVG_CUR    -1.211265              1.211265
25   TURNOVER_DYNAMIC_CUR_1M     0.408601              0.408601
14       TRANS_COUNT_ATM_PRC     0.380209              0.380209
21       REST_DYNAMIC_CUR_1M    -0.344919              0.344919
22             REST_AVG_PAYM    -0.278476              0.278476
31      REST_DYNAMIC_PAYM_1M    -0.236047              0.236047
35  TURNOVER_DYNAMIC_PAYM_1M     0.226137              0.226137
33          CLNT_SETUP_TENOR    -0.222930              0.222930
24       REST_DYNAMIC_CUR_3M    -0.155333              0.155333
9        TRANS_COUNT_SUP_PRC     0.141405              0.141405


#Feature importance for Random Forest


coefficients_rf = rf.feature_importances_[0]

# Create a DataFrame to store feature names and their coefficients
feature_coefficients_rf = pd.DataFrame({'Feature': X_train.columns, 'Coefficient': coefficients})

# Sort the DataFrame by absolute coefficient values to identify the most important features
feature_coefficients_rf['Absolute Coefficient'] = np.abs(feature_coefficients_rf['Coefficient'])
feature_coefficients_rf = feature_coefficients_rf.sort_values(by='Absolute Coefficient', ascending=False)

# Print the top features
print("Top features:")
print(feature_coefficients_rf.head(10))  # Adjust the number of top features as needed

# Plot feature coefficients
plt.figure(figsize=(20, 16))
plt.barh(feature_coefficients_rf['Feature'], feature_coefficients_rf['Coefficient'])
plt.xlabel('Coefficient Value')
plt.ylabel('Feature')
plt.title('Feature Coefficients')
plt.show()

Top features:
                     Feature  Coefficient  Absolute Coefficient
7               REST_AVG_CUR    -1.211265              1.211265
25   TURNOVER_DYNAMIC_CUR_1M     0.408601              0.408601
14       TRANS_COUNT_ATM_PRC     0.380209              0.380209
21       REST_DYNAMIC_CUR_1M    -0.344919              0.344919
22             REST_AVG_PAYM    -0.278476              0.278476
31      REST_DYNAMIC_PAYM_1M    -0.236047              0.236047
35  TURNOVER_DYNAMIC_PAYM_1M     0.226137              0.226137
33          CLNT_SETUP_TENOR    -0.222930              0.222930
24       REST_DYNAMIC_CUR_3M    -0.155333              0.155333
9        TRANS_COUNT_SUP_PRC     0.141405              0.141405


print(df.columns)
# Plot feature importance
plt.figure(figsize=(10, 6))
# plt.barh(df.columns, decision_tree.feature_importances_, color='blue', label='Decision Tree')
# plt.barh(df.columns, rf.feature_importances_, color='green', alpha=0.5, label='Random Forest')

plt.barh(feature_coefficients_dt['Feature'], feature_coefficients_dt['Coefficient'], color='blue', label='Decision Tree')
plt.barh(feature_coefficients_rf['Feature'], feature_coefficients_rf['Coefficient'], color='green', alpha=0.5, label='Random Forest')
plt.xlabel('Importance Score')
plt.title('Feature Importance Comparison')
plt.legend()
plt.tight_layout()
plt.show()

Index(['CR_PROD_CNT_IL', 'AMOUNT_RUB_CLO_PRC', 'TURNOVER_DYNAMIC_IL_1M',
       'AMOUNT_RUB_SUP_PRC', 'REST_DYNAMIC_FDEP_1M', 'REST_DYNAMIC_SAVE_3M',
       'CR_PROD_CNT_VCU', 'REST_AVG_CUR', 'AMOUNT_RUB_NAS_PRC',
       'TRANS_COUNT_SUP_PRC', 'TRANS_COUNT_NAS_PRC', 'CR_PROD_CNT_TOVR',
       'CR_PROD_CNT_PIL', 'TURNOVER_CC', 'TRANS_COUNT_ATM_PRC',
       'AMOUNT_RUB_ATM_PRC', 'TURNOVER_PAYM', 'CR_PROD_CNT_CC',
       'REST_DYNAMIC_FDEP_3M', 'REST_DYNAMIC_IL_1M', 'CR_PROD_CNT_CCFP',
       'REST_DYNAMIC_CUR_1M', 'REST_AVG_PAYM', 'LDEAL_GRACE_DAYS_PCT_MED',
       'REST_DYNAMIC_CUR_3M', 'TURNOVER_DYNAMIC_CUR_1M',
       'REST_DYNAMIC_PAYM_3M', 'REST_DYNAMIC_IL_3M', 'CNT_TRAN_ATM_TENDENCY3M',
       'TURNOVER_DYNAMIC_IL_3M', 'SUM_TRAN_ATM_TENDENCY3M',
       'REST_DYNAMIC_PAYM_1M', 'TURNOVER_DYNAMIC_CUR_3M', 'CLNT_SETUP_TENOR',
       'TURNOVER_DYNAMIC_PAYM_3M', 'TURNOVER_DYNAMIC_PAYM_1M',
       'TRANS_AMOUNT_TENDENCY3M', 'TRANS_CNT_TENDENCY3M', 'REST_DYNAMIC_CC_1M',
       'TURNOVER_DYNAMIC_CC_1M', 'REST_DYNAMIC_CC_3M',
       'TURNOVER_DYNAMIC_CC_3M', 'TARGET', 'PACK_101', 'PACK_102', 'PACK_103',
       'PACK_104', 'PACK_105', 'PACK_107', 'PACK_108', 'PACK_109', 'PACK_301',
       'PACK_K01', 'PACK_M01', 'PACK_O01', 'total_turnover', 'TOTAL_PROD_CNT',
       'AGE_GROUP_Group 1', 'AGE_GROUP_Group 2', 'AGE_GROUP_Group 3',
       'AGE_GROUP_Group 4', 'AGE_GROUP_Group 5'],
      dtype='object')


#empirical tuning using grid search for decision tree


# Define hyperparameters grid for Decision Tree
param_grid_dt = {
    'max_depth': [None, 5, 10],
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'max_features': ['auto', 'sqrt']
}

# Initialize DecisionTreeClassifier
dt_classifier = DecisionTreeClassifier()

# Perform Grid Search
grid_search_dt = GridSearchCV(estimator=dt_classifier, param_grid=param_grid_dt, cv=5)
grid_search_dt.fit(f_train_scaled, t_train_undersampled)

# Print best parameters and best score for Decision Tree
print("Best Parameters for Decision Tree:", grid_search_dt.best_params_)
print("Best Score for Decision Tree:", grid_search_dt.best_score_)

# Predictions on the training set using the Decision Tree model using Grid Search
y_train_pred_grdt = grid_search_dt.predict(f_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob_grdt = grid_search_dt.predict_proba(f_train_scaled)[:, 1]

# Compute the confusion matrix on the training set for Decision Tree using Grid Search
cm_train_grdt = confusion_matrix(t_train_undersampled, y_train_pred_grdt)

# Compute accuracy, precision, recall, and F1 score on the training set for Decision Tree using Grid Search
accuracy_train_grdt = (cm_train_grdt[0, 0] + cm_train_grdt[1, 1]) / np.sum(cm_train_grdt)
precision_train_grdt = precision_score(t_train_undersampled, y_train_pred_grdt)
recall_train_grdt = recall_score(t_train_undersampled, y_train_pred_grdt)
f1_train_grdt = f1_score(t_train_undersampled, y_train_pred_grdt)

# Compute the ROC AUC score on the training set for Decision Tree using Grid Search
roc_auc_train_grdt = roc_auc_score(t_train_undersampled, y_train_prob_grdt)

# Compute ROC curve for training set for Decision Tree using Grid Search
fpr_train_grdt, tpr_train_grdt, _ = roc_curve(t_train_undersampled, y_train_prob_grdt)

print("Confusion Matrix (Training Set):\n", cm_train_grdt)
print("Accuracy (Train):", accuracy_train_grdt)
print("Precision (Train):", precision_train_grdt)
print("Recall (Train):", recall_train_grdt)
print("F1 Score (Train):", f1_train_grdt)
print("ROC AUC Score (Train):", roc_auc_train_grdt)

# Predictions on the test set using the final Decision Tree model using Grid Search
y_test_pred_grdt = grid_search_dt.predict(f_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob_grdt = grid_search_dt.predict_proba(f_test_scaled)[:, 1]

# Compute the confusion matrix on the test set for Decision Tree using Grid Search
cm_test_grdt = confusion_matrix(t_test, y_test_pred_grdt)

# Compute accuracy, precision, recall, and F1 score on the test set for Decision Tree using Grid Search
accuracy_test_grdt = (cm_test_grdt[0, 0] + cm_test_grdt[1, 1]) / np.sum(cm_test_grdt)
precision_test_grdt = precision_score(t_test, y_test_pred_grdt)
recall_test_grdt = recall_score(t_test, y_test_pred_grdt)
f1_test_grdt = f1_score(t_test, y_test_pred_grdt)

# Compute the ROC AUC score on the test set for Decision Tree using Grid Search
roc_auc_test_grdt = roc_auc_score(t_test, y_test_prob_grdt)

print("Confusion Matrix (Test Set):\n", cm_test_grdt)
print("Accuracy (Test):", accuracy_test_grdt)
print("Precision (Test):", precision_test_grdt)
print("Recall (Test):", recall_test_grdt)
print("F1 Score (Test):", f1_test_grdt)
print("ROC AUC Score (Test):", roc_auc_test_grdt)

# Compute ROC curve for test set for Decision Tree using Grid Search
fpr_test_grdt, tpr_test_grdt, _ = roc_curve(t_test, y_test_prob_grdt)

# Plot ROC curve for both training and test sets for Decision Tree using Grid Search
plt.figure(figsize=(10, 5))
plt.plot(fpr_train_grdt, tpr_train_grdt, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train_grdt:.2f})')
plt.plot(fpr_test_grdt, tpr_test_grdt, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test_grdt:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()

Best Parameters for Decision Tree: {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2}
Best Score for Decision Tree: 0.6973294725133191
Confusion Matrix (Training Set):
 [[13294  7040]
 [ 3678 16656]]
Accuracy (Train): 0.7364512638929871
Precision (Train): 0.7029034436191762
Recall (Train): 0.8191206845677191
F1 Score (Train): 0.7565750624574153
ROC AUC Score (Train): 0.8120586207869641
Confusion Matrix (Test Set):
 [[60419 37547]
 [ 1822  6769]]
Accuracy (Test): 0.6305357695881078
Precision (Test): 0.1527439299575774
Recall (Test): 0.7879175881736701
F1 Score (Test): 0.2558829644470486
ROC AUC Score (Test): 0.7565873346583987


# Define hyperparameters grid for Random Forest
param_grid_dt = {
    'n_estimators': [50],
    'max_depth': [10],
    'min_samples_split': [5],
    'min_samples_leaf': [2],
    'max_features': ['auto', 'sqrt']
}

# Initialize Random Forest Classifier
rf_classifier = RandomForestClassifier()

# Perform Grid Search
grid_search_rf = GridSearchCV(estimator=rf_classifier, param_grid=param_grid_dt, cv=5)
grid_search_rf.fit(f_train_scaled, t_train_undersampled)

# Print best parameters and best score for Random Forest
print("Best Parameters for Decision Tree:", grid_search_rf.best_params_)
print("Best Score for Decision Tree:", grid_search_rf.best_score_)

# Predictions on the training set using the Random Forest model using Grid Search
y_train_pred_grrf = grid_search_rf.predict(f_train_scaled)

# Probabilities for the positive class on the training set
y_train_prob_grrf = grid_search_rf.predict_proba(f_train_scaled)[:, 1]

# Compute the confusion matrix on the training set for Random Forest using Grid Search
cm_train_grrf = confusion_matrix(t_train_undersampled, y_train_pred_grrf)

# Compute accuracy, precision, recall, and F1 score on the training set for Random Forest using Grid Search
accuracy_train_grrf = (cm_train_grrf[0, 0] + cm_train_grrf[1, 1]) / np.sum(cm_train_grrf)
precision_train_grrf = precision_score(t_train_undersampled, y_train_pred_grrf)
recall_train_grrf = recall_score(t_train_undersampled, y_train_pred_grrf)
f1_train_grrf = f1_score(t_train_undersampled, y_train_pred_grrf)

# Compute the ROC AUC score on the training set for Random Forest using Grid Search
roc_auc_train_grrf = roc_auc_score(t_train_undersampled, y_train_prob_grrf)

# Compute ROC curve for training set for Random Forest using Grid Search
fpr_train_grrf, tpr_train_grrf, _ = roc_curve(t_train_undersampled, y_train_prob_grrf)

print("Confusion Matrix (Training Set):\n", cm_train_grrf)
print("Accuracy (Train):", accuracy_train_grrf)
print("Precision (Train):", precision_train_grrf)
print("Recall (Train):", recall_train_grrf)
print("F1 Score (Train):", f1_train_grrf)
print("ROC AUC Score (Train):", roc_auc_train_grrf)

# Predictions on the test set using the final Random Forest model using Grid Search
y_test_pred_grrf = grid_search_rf.predict(f_test_scaled)

# Probabilities for the positive class on the test set
y_test_prob_grrf = grid_search_rf.predict_proba(f_test_scaled)[:, 1]

# Compute the confusion matrix on the test set for Random Forest using Grid Search
cm_test_grrf = confusion_matrix(t_test, y_test_pred_grrf)

# Compute accuracy, precision, recall, and F1 score on the test set for Random Forest using Grid Search
accuracy_test_grrf = (cm_test_grrf[0, 0] + cm_test_grrf[1, 1]) / np.sum(cm_test_grrf)
precision_test_grrf = precision_score(t_test, y_test_pred_grrf)
recall_test_grrf = recall_score(t_test, y_test_pred_grrf)
f1_test_grrf = f1_score(t_test, y_test_pred_grrf)

# Compute the ROC AUC score on the test set for Random Forest using Grid Search
roc_auc_test_grrf = roc_auc_score(t_test, y_test_prob_grrf)

print("Confusion Matrix (Test Set):\n", cm_test_grrf)
print("Accuracy (Test):", accuracy_test_grrf)
print("Precision (Test):", precision_test_grrf)
print("Recall (Test):", recall_test_grrf)
print("F1 Score (Test):", f1_test_grrf)
print("ROC AUC Score (Test):", roc_auc_test_grrf)

# Compute ROC curve for test set for Random Forest using Grid Search
fpr_test_grrf, tpr_test_grrf, _ = roc_curve(t_test, y_test_prob_grrf)

# Plot ROC curve for both training and test sets for Random Forest using Grid Search
plt.figure(figsize=(10, 5))
plt.plot(fpr_train_grrf, tpr_train_grrf, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train_grrf:.2f})')
plt.plot(fpr_test_grrf, tpr_test_grrf, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test_grrf:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()

Best Parameters for Decision Tree: {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}
Best Score for Decision Tree: 0.7466066707377845
Confusion Matrix (Training Set):
 [[14855  5479]
 [ 3077 17257]]
Accuracy (Train): 0.7896134552965477
Precision (Train): 0.7590165376495426
Recall (Train): 0.8486770925543424
F1 Score (Train): 0.8013466449965173
ROC AUC Score (Train): 0.8688035132941526
Confusion Matrix (Test Set):
 [[66241 31725]
 [ 1659  6932]]
Accuracy (Test): 0.6867028914102311
Precision (Test): 0.17932069224202601
Recall (Test): 0.806890932371086
F1 Score (Test): 0.2934304097527938
ROC AUC Score (Test): 0.8110961011696805


#Ramdom Forest is giving good accuracy for test data after doing hyper perameter tunning

Our goal is to predict the Target in the Bank Churn Dataset using Machine Learning algorithms¶

Let's do Data Cleaning¶

Let's start EDA¶

Univariate Analysis¶

Bivariate Analysis¶

Let's do correlation¶

Feature Engineering¶

Let's split the data¶

Let's perform Scaling¶

Logistic Regression¶

Performing Sensitivity Analysis on the Logistic regression¶

RESULTS After Sensitivity Analysis on Logistic Regression¶

SVM¶

LIME¶

SHAP¶

	ID	CR_PROD_CNT_IL	AMOUNT_RUB_CLO_PRC	PRC_ACCEPTS_A_EMAIL_LINK	APP_REGISTR_RGN_CODE	PRC_ACCEPTS_A_POS	PRC_ACCEPTS_A_TK	TURNOVER_DYNAMIC_IL_1M	CNT_TRAN_AUT_TENDENCY1M	SUM_TRAN_AUT_TENDENCY1M	...	REST_DYNAMIC_CC_3M	MED_DEBT_PRC_YWZ	LDEAL_ACT_DAYS_PCT_TR3	LDEAL_ACT_DAYS_PCT_AAVG	LDEAL_DELINQ_PER_MAXYWZ	TURNOVER_DYNAMIC_CC_3M	LDEAL_ACT_DAYS_PCT_TR	LDEAL_ACT_DAYS_PCT_TR4	LDEAL_ACT_DAYS_PCT_CURR	TARGET
0	146841	0	0.000000	NaN	NaN	NaN	NaN	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
1	146842	0	0.041033	NaN	NaN	NaN	NaN	0.0	0.166667	0.186107	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
2	146843	0	0.006915	0.0	NaN	0.0	0.0	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
3	146844	0	0.000000	NaN	NaN	NaN	NaN	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
4	146845	0	0.000000	NaN	NaN	NaN	NaN	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
355185	590822	0	0.000000	0.0	NaN	0.0	0.0	0.0	0.142857	0.123579	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0
355186	590823	0	0.000000	0.0	NaN	0.0	0.0	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
355187	590825	0	0.041298	NaN	NaN	NaN	NaN	0.0	0.089286	0.065293	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
355188	590826	0	0.000000	NaN	NaN	NaN	NaN	0.0	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0
355189	590828	0	0.000000	NaN	NaN	NaN	NaN	0.0	NaN	NaN	...	0.0	1.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0	1